Information theoretic genomics enabled hyperscalability

Abstract

To provide a method, system and ecosystem security platform for implementing an application-specific security architecture based on a genome network topology interoperable with modern application and network stacks.SOLUTION: The CG technology is supported by a wide range of digital capability platforms and component configurations and is configured to ensure the orderly execution of important computationally complex genomic construction and digital DNA regulatory functions and processes, and the CG shows computational complexity by enabling virtual non-constraint by generating an information-theoretically constructed genome structure. CG digital rendering greatly expands the range of its unique differences and correlations while maintaining the biochemically unlimited properties, and CG retains the ability to strategically control different digital DNA-based genome structures without compromising computational integrity.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 62 / 970,304, filed February 5, 2020, entitled "Genome-Based Security Platform," the contents of which are incorporated herein by reference in their entirety.

[0002] This disclosure relates to information-theoretically driven security platforms and corresponding digital genome structures that exhibit controlled entropy but undergo digital modification and reconfiguration by computationally complex functions and processes without losing genome integrity. These structures enable the formation of comprehensively secure, hyper-scalable digital ecosystems, enclaves, and / or digital cohorts with a mutual identity of interest, enabling application-specific security architectures based on genome network topologies that are interoperable with modern applications and network stacks. [Background technology]

[0003] The requirement to distinguish between the noble and the nefarious became urgent as neighboring nodes of the ARPAnet breached their boundaries and became a World Wide Web of virtual digital ecosystems underpinned by an interoperable Digital Monoculture. By the time the significant implications facing this new machine-connected world became clear, first-responder technologies (firewalls, analytics, forensics, PKI, proxies, monitoring, etc.) had already been relegated to patrolling network perimeters and providing rapid restoration services.

[0004] It is widely accepted among experts that cryptography is the only provable security solution for the machine-connected world. Research into quantum cryptography, homomorphic encryption, and obfuscated cryptography has attracted significant investment but has not had the impact expected. Nevertheless, the most essential yet essential complexity of all cryptographic fields—hyperscalability—remains untapped. Numerous efforts are underway to update PKI to a post-quantum state, despite its persistence of linear scalability. Summary of the Invention

[0005] Applicant has developed and disclosed herein Cyphergenics (CG) technology, a comprehensive solution to the previously intractable hyperscalability dilemma. As will be described, Cyphergenics enables unlimited deployment of cryptography-based digital ecosystem security with minimal overhead and bandwidth, while fully preserving interoperability. Importantly, hyperscalability directly facilitates functionally homomorphic encryption and functionally indistinguishable obfuscation.

[0006] The artificial separation between attacks on cyberinfrastructure and attacks on privacy has been and continues to be misguided, as both exploit the same digital ecosystem and digital monoculture. Security solutions, despite their impressive adaptation and extension of traditional security technologies and techniques, remain pedestrian klugs, experts in post-mortems and poor at prevention. These technologies are notoriously limited in their ability to thwart sabotage, espionage, embezzlement, piracy, and privacy violations (even covert surveillance). The digital attack surface is ever-increasing, and weaponized malware, powerful new processors, and ultimately, disruptive algorithms powered by quantum computer-based cryptography and artificial intelligence, pose new levels of devastation.

[0007] Authentic engagements drive a previously unimaginable virtualization of products, services, and knowledge, redefining efficiency and effectiveness. Meanwhile, within the same network-centric campus, illicit engagements facilitate devastating cyberattacks (sabotage, espionage, interception, piracy, etc.) and pervasive attacks on privacy (covert mass surveillance, profiling, assimilation, etc.). The common machine language and inherent hyperscalability essential to the network-centric mission make the noble and the nefarious virtually indistinguishable.

[0008] Applicant proposes that the most important network-centric capability is not the digital ecosystem of diverse digital cohorts (e.g., networks, grids, clouds, systems, devices, appliances, sensors, IoT, applications, files, data, etc.) nor their interoperable digital monoculture, but the common machine language they share and their hyper-scalability to extend ubiquitous, on-demand engagement (e.g., connection, communication, collaboration, coordination, etc.) that accounts for the vast permutations of hundreds of billions of unattended security instances, billions of cohorts, millions of control points, and hundreds of billions of security instances per day.

[0009] Reconstructing the digital ecosystem and the digital monoculture would remain impractical, endlessly disruptive, and financially imprudent. While technologies that can modify the common machine language based on computationally quantum-proven cryptography would provide highly effective security, they would also eliminate the hyperscalability essential to the interoperability of the digital monoculture.

[0010] CipherGenics (CG), an entirely new technology based on computationally complex genome structures, liberates modern cryptography from its esoteric yet powerful computationally complex foundations. In embodiments, CG enables virtual unconstraints by generating information-theoretically constructed genome structures, demonstrating computational complexity. CG digital renderings are not complex structures that can be directly controlled like biochemistry. Importantly, CG digital renderings retain the unlimited properties of biochemistry while significantly expanding its inherent range of differences and correlations. CG preserves the ability to strategically control genome structures based on different digital DNA without compromising computational integrity. Figure 1 illustrates attributes of a CipherGenics-based digital ecosystem in relation to the related attributes of organic ecosystems and modern digital ecosystems, according to some embodiments of the present disclosure.

[0011] In embodiments, unique genomic and cryptographic properties, combined with information-theoretic principles, enable CipherGenics-based technologies to achieve hyper-scalability with unlimited differentiation (affiliation) and correlation (authentication). These properties create virtual affiliation, virtual authentication, virtual agility, virtual organic engagement, and virtual effective domains of trust, powerful attributes that extend far beyond security.

[0012] In embodiments, CipherGenics enables hyper-scalability of specific digital ecosystems, enclaves, and cohorts, with mutual identities of interest (digital DNA related and regulated) forming the dynamic basis of their engagement. These domain-resident ecosystems, enclaves, and cohorts engage based on hyper-scalable digital data objects and digital coder objects that exhibit unique, non-repeatable, and computationally tolerant attributes reflecting mutual identities of interest called Virtual Binary Language Scripts (VBLS), and share malicious intent without hesitation.

[0013] While CipherGenics (CG) technology can be supported by a wide range of digital capability platforms and component configurations, some embodiments of the present disclosure are configured to ensure the orderly execution of critical computationally complex genome construction and digital DNA regulatory functions and processes. In embodiments, the CipherGenics Ecosystem Security Platform (CG-ESP) may be comprised of modules that control specific computational and genome construction and digital DNA regulatory functions. In embodiments, this adaptability is important given that CipherGenics functionality may be rendered cryptographically, without any cryptography, or in combination.

[0014] In embodiments, CipherGenix supports applications beyond network-centric interests, as its modular rendering can serve multiple purposes. For example, they allow for the reimagining and incremental improvement or modification of the architectural and regulatory processes and functions enabled by individual genome information theory without compromising hyperscalability, and they enable computational and functional innovation among CipherGenix application-ready attributes.

[0015] Security applications almost always need to tolerate network configurations, such as the widespread depletion of IP-IV addresses due to NAT evasion for IP-SEC security. CipherGenics VBLS attributes enable powerful new security application-centric genome network topologies to operate concurrently, interoperably, and on-demand on existing network configurations. CipherGenics enables many security-centric genome network topologies, including Directed Architectures, Spontaneous Architectures, Ephemeral Architectures, and Interledger Architectures. Figure 2 illustrates one example of this adaptability, showing a CipherGenics-enabled security stack that can be applied concurrently at various layers of commonly known application and network stacks, in accordance with some embodiments of the present disclosure, and an example of a CipherGenics-facilitated genome architecture for a digital ecosystem that can result from such application.

[0016] In embodiments, the scope of genome construction enabled by CipherGenics' information theory allows digital cohorts to be produced as the offspring of particular corporations and / or enclaves prior to their own conception. In embodiments, CipherGenics-enabled digital ecosystems can be rendered gnomically flat or hierarchical, with or without an orientation to order, e.g., time. In embodiments, CipherGenics cohorts can serve as carriers of the Cambrian genome.

[0017] The differences between ciphergenics, the implementation and practice of which is described in the details of this patent, and the exploration of postmodern (quantum proof) cryptography are summarized below, and examples can be observed in Figure 3. The challenges of network-centric security are expected to become increasingly serious in the future, but countering quantum computer-based cryptography cryptanalysis is nothing more than maintaining the status quo.

[0018] In embodiments, CypherGenics-based technology can replace an underlying approach as opposed to developing a variant of an existing technology and its inherent limitations.

[0019] The present disclosure relates to different embodiments of CipherGenics-based technology and security platforms for application in a myriad of digital ecosystems with a wide range of different inter-identities and topologies of interest. In embodiments of the present disclosure, instances of the CipherGenics-based security platform can be configured for different types of digital ecosystems with different architectures to optimize different aspects of the respective ecosystems they serve.

[0020] According to some embodiments of the present disclosure, an ecosystem security platform is disclosed. The ecosystem security platform is executed by a processing system of a VDAX. The ecosystem security platform includes a root DNA module, a link module, a sequence mapping module, and a binary conversion module. The root DNA module is configured to manage a digitally generated genome dataset assigned to the VDAX. The genome dataset is specific to the VDAX and includes a genome eligibility object, a genome correlation object, and a genome differentiation object. The root DNA module is configured to modify the genome dataset using one or more computationally complex functions. The link module is configured to receive a link from a second VDAX. The link includes an encoded genome adjustment instruction. The link module is configured to decode the link based on the genome eligibility object and the modified genome correlation object to obtain the decoded genome adjustment instruction. The modified genome correlation object is modified from the genome correlation object by the root DNA module. The sequence mapping module is configured to obtain a sequence from a digital object to be provided to the second VDAX. The sequence is extracted from a first portion of the digital object. The sequence mapping module is configured to map the sequence to a modified genome differentiation object to obtain a genome engagement factor. The modified genome differentiation object is modified from the genome differentiation object by the root DNA module based on the decoded genome regulatory instructions. The binary conversion module is configured to encode a second portion of the digital object based on the genome engagement factor to obtain an encoded digital object. The binary conversion module is configured to generate a virtual binary language script (VBLS) object including the encoded digital object and the first portion of the digital object.The VBLS object is provided to a second VDAX as part of a series of VBLS objects.

[0021] In some embodiments, the digital object may be part of a series of digital objects that are each encoded into a series of respective VBLS objects. Each digital object may be encoded with a different respective genome engagement factor. In some embodiments, a sequence mapping module may obtain respective sequences from each digital object and map each sequence to the modified genome differentiation object to obtain respective genome engagement factors that can be used to encode the respective digital object. In some embodiments, the series of VBLS objects may be in a non-recurring language specific to the VDAX and the second VDAX. In some embodiments, the sequences may be public sequences defined in the first portion of the digital object according to a known protocol and format. In some embodiments, the sequences may be private sequences that may be defined in the first portion of the digital object according to a proprietary protocol and format that may not be publicly available.

[0022] In some embodiments, the binary transformation module may include a disambiguation module that encodes a second portion of the digital object based on the genome engagement factor. It may also include an encryption module that encrypts the second portion of the digital object based on the genome engagement factor by determining an exclusive-OR of the second portion and the genome engagement factor using an XOR operation. In some embodiments, the binary transformation module may include an encryption module that encrypts the second portion of the digital object based on the genome engagement factor by encrypting the second portion of the digital object with a cryptographic function and the genome engagement factor.

[0023] In some embodiments, the link module may decode a link from the second VDAX as part of a link exchange process with the second VDAX. The link exchange process may be a one-time process. In some embodiments, the link exchange process may include authenticating an ecosystem member associated with the second VDAX based on the VDAX's genome eligibility object. In some embodiments, the link module may be further configured to generate a second link including second encoded genome regulatory instructions. The second link may be provided to the second VDAX as part of the link exchange process. In some embodiments, the link exchange process may be a two-way symmetric process such that the second link may be generated independently of the link received from the second VDAX. In some embodiments, in generating the second link, the link module may be further configured to determine the second genome regulatory instructions. The link module may be further configured to encode the second genome regulatory instructions using a link genome engagement coefficient to obtain the second encoded genome regulatory instructions. The link genome engagement factor may be determined by the sequence mapping module based on the link mapping sequence and a second modified genome correlation object that may be modified from the genome correlation object by the root DNA module. The link module may be further configured to generate a genome engagement cargo including the link mapping sequence, second encoded genome regulatory instructions, and encoded link decoding information. The link mapping sequence may be unobfuscated in the genome engagement cargo, and the second link may include the genome engagement cargo. The link module may be further configured to provide the second link to a second VDAX. The second VDAX may decode the second encoded genome regulatory instructions based on the link mapping sequence, the encoded link decoding information, and a second genome dataset assigned to the second VDAX. In some embodiments, the link mapping sequence may remain unencoded in the genome engagement cargo.In some embodiments, the binary conversion module may be further configured to receive a second VBLS object from the second VDAX. The second VBLS object may include a second encoded digital object and unencoded metadata. The binary conversion module may be further configured to decode the second digital object based on a second genome engagement factor to obtain an unencoded second digital object. The second genome engagement factor may be determined by the sequence mapping module based on the second sequence and a second modified genome differentiation object that may be derived from the genome differentiation object of the genome dataset by the root DNA module based on the second genome adjustment instructions.

[0024] In some embodiments, the link module may be configured to perform a link exchange process across a set of interoperable digital communication media. In some embodiments, the link module may be configured to perform a link exchange process across a set of interoperable digital networks. In some embodiments, the link module may be configured to perform a link exchange process across a set of interoperable digital devices. In some embodiments, the link module may be configured to perform a link exchange that may be performed asynchronously with respect to a second VDAX. In some embodiments, the link module may be configured to perform a link exchange process in a symmetric manner, such that a VDAX may not provide a second link to the second VDAX. In some embodiments, the link module may be further configured to confirm an eligibility correlation with respect to the second VDAX based on the VDAX's genomic eligibility correlation object. In some embodiments, the link module may be further configured to confirm a link exchange correlation with respect to the second VDAX based on the link received from the second VDAX and the VDAX's genomic correlation object. In some embodiments, the link module may be configured to perform a secure exchange of link information using a set of computationally complex functions. The set of computationally complex functions may be one of cryptography-based functions, cryptography-free functions, or hybrid functions that may include at least one cryptography-based function and at least one cryptography-free function. In some embodiments, the link module may be configured to perform a series of processes to securely exchange link information with a second VDAX and enable two-way symmetric engagement. The exchange of link information may exhibit the same level of entropy as asymmetric engagement. In some embodiments, the link module may include a static link module that may be configured to generate and decrypt static links. The static link module may generate static links according to rules and processes defined by a best-in-class ecosystem VDAX within the digital ecosystem.In some embodiments, the link module may include a dynamic link module that may be configured to generate and decode a dynamic link. The link received from the second VDAX may be a dynamic link that further includes an instruction set that, when executed by the VDAX, may overwrite the configuration of at least one of the root DNA module, the sequence mapping module, or the binary conversion module. The modified configuration may be executed only when generating a VBLS that may be provided to the second VDAX.

[0025] In some embodiments, the sequence mapping module may be configured to select public sequences from a respective first portion of the respective digital objects, which may be formatted according to a publicly available protocol. In some embodiments, when mapping sequences to modified genome differentiation objects, the sequence mapping module may: process the sequences to derive intermediate values; the sequence mapping module may be configured to generate genome engagement factors based on the intermediate values ​​and the modified genome differentiation object using a set of information-theoretically facilitated computational complexity functions; in exemplary embodiments, the set of information-theoretically facilitated computational complexity functions may be one of cryptography-based functions, cryptography-free functions, or hybrid functions including at least one cryptography-based function and at least one cryptography-free function; in exemplary embodiments, the genome engagement factors may be binary vectors exhibiting specific entropy; the specific entropy of the genome engagement factors may be equal to or greater than the intrinsic entropy of the sequence; in exemplary embodiments, the sequence mapping module may be configured to select sequences from a respective first portion of the respective digital objects, which may be formatted according to a proprietary protocol.

[0026] In some embodiments, the root DNA module may include a CNA module that may formulate and build a genome eligibility object. The CNA module may be configured to employ information-theoretically driven genome processes to establish specific relationships with other VDAXs within their respective digital ecosystems. In some embodiments, the link module may use the genome eligibility object to confirm genome engagement alignment with a second VDAX. In some embodiments, the genome eligibility object may be a CNA object that exhibits a specific entropy. The CNA object may enable genome processing based on difference and correlation. The CNA object may be an N-dimensional binary vector that exhibits a specific entropy. The specific entropy of the CNA object may be configurable by a community owner. In some embodiments, the CNA module may be configured to generate a respective set of CNA objects based on the PNA object of the VDAX. Each set of CNA objects may be assigned to a respective set of descendant VDAXs, and each CNA object may exhibit a specific entropy equal to the entropy of the CNA object of the VDAX. In some embodiments, the CNA module may be configured to process eligibility correlations for other VDAXs based on CNA objects and engagement information provided by the other VDAXs using a set of information-theoretically facilitated computational complexity functions. The set of information-theoretically facilitated computational complexity functions may be one of cryptography-based functions, cryptography-free functions, or hybrid functions including at least one cryptography-based function and at least one cryptography-free function. In some embodiments, the CNA module may be configured to establish specific relationships between other VDAXs in the digital ecosystem to which the VDAX belongs based in part on the VDAXs' CNA objects.

[0027] In some embodiments, the root DNA module may include a PNA module that formulates and constructs a genome eligibility object. The PNA module may be configured to employ information-theoretically driven genome processes to establish specific relationships with other VDAXs within their respective digital ecosystems. In some embodiments, the link module may use the genome eligibility object to confirm genome engagement eligibility with a second VDAX. The genome eligibility object may be a PNA object that exhibits a specific entropy. The PNA object may enable genome processing based on difference and correlation. In some embodiments, the PNA object may include a first N-dimensional binary vector and a second N-dimensional binary vector that exhibit a specific entropy. The first N-dimensional vector may be composed of M randomly selected binary primitive polynomials of degree T such that M×T equals N, and the second N-dimensional vector is determined based on the first N-dimensional binary vector. In some embodiments, the specific entropy of the PNA object may be configurable by a community owner. In some embodiments, the PNA module may be configured to generate a set of respective PNA objects based on the PNA objects of the VDAX. Each set of PNA objects may be assigned to a respective set of descendant VDAXs, and each PNA object may exhibit a specific entropy equal to the entropy of the PNA object of the VDAX. In some embodiments, the PNA module may be configured to process eligibility correlations for other VDAXs based on the PNA objects and engagement information provided by the other VDAXs using a set of computational complexity functions based on information theory. The set of computational complexity functions based on information theory may be one of a cryptography-based function, a non-cryptography function, and a hybrid function including at least one cryptography-based function and at least one non-cryptography function.In some embodiments, the PNA module may be configured to establish certain relationships between other VDAXs within the digital ecosystem to which the VDAX belongs, based in part on the VDAX's PNA object.

[0028] In some embodiments, the root DNA module may include an LNA module that formulates and builds genome correlation objects. The LNA module may be configured to employ information-theoretically driven genome processes to establish specific relationships with other VDAXs within their respective digital ecosystems. In some embodiments, the link module may use genome eligibility objects to verify link exchange correlations with a second VDAX based on the link. In some embodiments, the genome eligibility objects may be LNA objects that exhibit a specific entropy. The LNA objects may enable differential and correlation-based genome processing that may be performed during link exchanges. The LNA objects may be sufficiently correlated with a second LNA object in the second VDAX. In some embodiments, the specific entropy of the LNA objects may be configurable by a community owner. In some embodiments, the LNA objects may be N-dimensional binary vectors that exhibit a specific entropy. In some embodiments, the LNA module may be configured to use a set of information-theoretically based computational complexity functions to search for link eligibility correlations with other VDAXs based on the LNA objects and genome engagement cargo provided in each link provided by the other VDAXs. In some embodiments, the set of information-theoretic computationally complex functions may be one of cryptography-based functions, cryptography-free functions, and hybrid functions including at least one cryptography-based function and at least one cryptography-free function. In some embodiments, the LNA module may be configured to establish specific relationships between other VDAXs in a digital ecosystem to which the VDAX belongs based in part on the VDAX's LNA objects. In some embodiments, the LNA module may be configured to generate respective sets of LNA objects based on the VDAX's LNA objects. The respective sets of LNA objects may be respectively assigned to respective sets of descendant VDAXs.Each LNA object may exhibit a particular entropy equal to the entropy of the LNA object of the VDAX. In some embodiments, the LNA module may be configured to modify the genome correlation object based on a particular set of instructions using a set of information-theoretically facilitated computationally complex functions. The set of information-theoretically facilitated computationally complex functions may be one of cryptography-based functions, cryptography-free functions, and hybrid functions including at least one cryptography-based function and at least one cryptography-free function. In some embodiments, the LNA module may modify the genome correlation object based on a set of instructions received from the ancestor VDAX. The modification of the genome correlation object may be used to establish future engagement with the digital ecosystem to which the VDAX belongs, while previously established engagement may not be affected. In some embodiments, the LNA module may modify the genome correlation object based on a set of instructions received from a second VDAX in the link to obtain a modified genome correlation object. The modified genome correlation object may be used to determine a link genome engagement factor, which may be used to decode the encoded GRI.

[0029] In some embodiments, the root DNA module may include an XNA module that performs genomic processes involving the genomic correlation objects, including at least one of generating new genomic correlation objects and modifying the genomic correlation objects of the VDAX. In some embodiments, the XNA module may be configured to employ information-theoretically driven genomic processes to modify the genomic differentiation objects of the VDAX in the same manner as the second VDAX according to genomic regulatory instructions deciphered from the link obtained from the second VDAX. In some embodiments, the genomic eligibility object may be an XNA object that exhibits a specific entropy. In some embodiments, the XNA object may be sufficiently correlated with a second XNA object of the second VDAX. In some embodiments, the specific entropy of the XNA object may be configurable by a community owner. In some embodiments, the XNA object may be an N-dimensional binary vector that exhibits a specific entropy. In some embodiments, the XNA object may be used to establish future differentiation from other VDAXs that possess sufficiently correlated XNA objects. In some embodiments, the XNA module may be configured to generate a set of respective XNA objects based on the XNA objects of the VDAX. Each set of XNA objects may be assigned to each set of descendant VDAXs. Each XNA object may exhibit a specific entropy equal to the entropy of the XNA objects in the VDAX. In some embodiments, the XNA module may be configured to modify the XNA based on a specific set of instructions using a set of information-theoretically driven computationally complex functions. In some embodiments, the XNA module may update the XNA based on a set of instructions received from an ancestor VDAX so that the updated XNA can be used to establish future differentiation against other VDAXs in the digital ecosystem to which the VDAX belongs.In some embodiments, the second VDAX may not be able to establish future differentiation from the VDAX unless the second VDAX possesses a sufficiently correlated and continuously updated XNA. In some embodiments, the set of information-theoretically accelerated computationally complex functions may be one of cryptography-based functions, cryptography-free functions, and hybrid functions including at least one cryptography-based function and at least one cryptography-free function.

[0030] In some embodiments, the ecosystem security platform may further include an authentication module that performs a secure genome-based engagement correlation with respect to the second VDAX according to an information-theoretically facilitated computationally complex function. The information-theoretically facilitated computationally complex function may be one of a cryptography-based function, a cryptography-free function, and a hybrid function that may include at least one cryptography-based function and at least one cryptography-free function. In some embodiments, the ecosystem security platform may further include a master integrity controller that may include a genome process controller, an authentication module, and an engagement instance module. The genome process controller may have a master controller genome dataset assigned to it.

[0031] In some embodiments, the genome process controller may be configured to engage with one or more platform modules to authenticate and verify the integrity of the one or more platform modules. In some embodiments, the genome process controller may verify and verify the integrity of one or more platform modules based on a master controller genome dataset and a set of computationally complex functions. In some embodiments, the genome process controller may verify and verify the integrity of one or more platform modules without determining any processes or functions performed by the one or more platform modules. In some embodiments, the genome process controller may be further configured to verify and verify the integrity of any underlying operational processes and functions connecting the one or more platform modules using a set of computationally complex functions. In some embodiments, the genome process controller may be further configured to verify, disqualify, or initiate changes to one or more platform modules and the underlying operational processes and functions.

[0032] In some embodiments, the authorization module may be configured to confirm or deny the operational configuration of other VDAXs in the digital ecosystem based on the genome controller genomic data and a set of information-theoretically driven computational complexity functions. In response to determining to deny the operational configuration of another VDAX, the authorization module may disqualify or initiate a modification of the operational configuration of the other VDAX.

[0033] In some embodiments, the engagement instance module may be configured to track security instances within the VDAX digital ecosystem according to a set of one or more engagement tracking policies that define one or more definitions of a security instance. In some embodiments, the engagement instance module may be further configured to determine the number of security instances within the VDAX digital ecosystem according to a set of one or more engagement account policies that each define how the security instances are counted. In some embodiments, the engagement instance module may be further configured to determine reporting of the number of security instances in the digital ecosystem to another VDAX according to a set of one or more engagement reporting policies that each define how the security instances are reported, to which VDAX the security instances are reported, and the frequency with which the security instances are reported.

[0034] In some embodiments, the VDAX is a member of a digital ecosystem. In some of these embodiments, the digital ecosystem is a cloud services system. In some of these embodiments, the digital ecosystem is an enterprise information technology system. In some of these embodiments, the digital ecosystem is a computing device, and each descendant VDAX corresponds to a hardware component and a digital component of the computing device. In some of these embodiments, the digital ecosystem is a classified computing infrastructure. In some embodiments, the digital ecosystem is a traffic grid. In some embodiments, the digital ecosystem is a home network.

[0035] According to some embodiments of the present disclosure, a system for implementing genome security-related controls for a digital ecosystem is disclosed. The system includes an Ecosystem VDAX executed by a processing system associated with an owner of the digital ecosystem. The Ecosystem VAX is configured to maintain an ancestral genome dataset corresponding to the digital ecosystem, the ancestral genome dataset including one or more different digitally generated ancestral genome data objects, each ancestral genome data object exhibiting a respective specific entropy. The Ecosystem VAX is further configured to generate a plurality of respective descendant genome datasets based on the ancestral genome dataset, each descendant genome dataset including one or more different descendant genome data objects each derived from one or more digitally generated ancestral genome data objects and each exhibiting a respective specific entropy of the ancestral genome data objects from which it was derived. In these embodiments, the Ecosystem VDAX is configured, for each respective descendant genomic dataset, to assign the descendant genomic dataset to a respective descendant VDAX of the plurality of descendant VDAXs, and the descendant VDAXs are configured to establish unique, non-recurrent engagement with other descendant VDAXs in the digital community based on the respective descendant genomic dataset assigned to the descendant VDAX without further interaction from the Ecosystem VDAX. The Ecosystem VDAX is further configured to control the genomic topology of the digital ecosystem by selectively updating one or more of the descendant genomic datasets to affect the ability of a particular descendant VDAX to engage with other VDAXs in the digital ecosystem.

[0036] In some embodiments, the ancestral genome dataset includes ancestral genome differentiation objects, and each ancestral genome dataset includes a respective descendant genome differentiation object. In some of these embodiments, pairs of descendant VDAXs from a plurality of descendant VDAXs can exchange virtual binary language scripts (VBLS) only if the respective descendant genome differentiation objects of the descendant VDAX pairs are sufficiently correlated.

[0037] In some of these embodiments, future VBLS replacement can be prevented if the first descendant genome differentiation object of the first descendant VDAX in the set of descendant VDAXs is updated and the second descendant genome differentiation object of the second descendant VDAX in the set of descendant VDAXs is not updated.

[0038] In some embodiments, the ancestor genome differentiation object and each descendant genome differentiation object are XNA objects. In some of these embodiments, the digital ecosystem is at least one of a static ecosystem in which the ecosystem platform is configured according to a directed architecture, an interactive ecosystem in which the ecosystem platform is configured according to a free-form architecture, or a dynamic ecosystem in which the ecosystem platform is configured according to a dynamic state spontaneous architecture. In some of these embodiments, the digital ecosystem is a set of one or more enclave VDAXs, each enclave VDAX corresponding to a respective digital enclave of the digital ecosystem, the enclave VDAX being assigned a respective enclave-specific XNA object that controls the genome topology of the respective enclave. In some of these embodiments, each respective digital enclave includes one or more descendant VDAXs each representing one or more respective cohorts admitted to the digital enclave, and each descendant VDAX included in each digital enclave is assigned a descendant enclave-specific XNA object that is derived from the enclave VDAX's enclave-specific XNA object and that sufficiently correlates with each descendant enclave XNA object of other descendant VDAXs included in the digital enclave. In some of these embodiments, each enclave VDAX controls membership to a corresponding respective digital enclave by assigning enclave-specific XNA objects to the digital enclave's cohorts.

[0039] In some embodiments, each enclave VDAX further allocates a respective enclave genome correlation object derived from the descendant correlation object of the descendant genome dataset. In some of these embodiments, each descendant VDAX of a digital enclave allocates a respective descendant enclave-specific genome correlation object derived from the enclave from a respective enclave genome correlation object of the digital enclave's enclave VDAX. In some of these embodiments, each descendant VDAX allocates a respective enclave-specific genome correlation object directly from the digital enclave's enclave VDAX. In some embodiments, each descendant VDAX allocates a respective enclave-specific genome correlation object directly from the ecosystem VDAX.

[0040] In some embodiments, each descendant VDAX uses its respective descendant enclave-specific genomic correlation object to spawn links that each establish a unique, non-recursive engagement with other descendant VDAXs formed with respect to their respective digital enclaves. In some embodiments, each link spawned by a descendant VDAX provides unique genomic regulatory instructions that define how the link-hosting descendant VDAX modifies its enclave-specific XNA object to generate a non-recursive VBLS that only the descendant VDAX can decipher. In some embodiments, each descendant VDAX uses its respective descendant enclave-specific genomic correlation object to host links provided by other descendant VDAXs in the digital ecosystem, and the other descendant VDAXs provide links to the descendant VDAXs to establish unique, non-recursive engagements with the descendant VDAXs with respect to their respective digital enclaves.

[0041] In some of these embodiments, each hosted link by a descendant VDAX provides unique genome regulatory instructions that define how the descendant VDAX modifies its enclave-specific genome differentiation object to generate non-recurrent VBLS that can only be deciphered by the other descendant VDAX that provided the link.

[0042] In some embodiments, an enclave VDAX controls a genomic network topology by selectively updating descendant enclave-specific genomic data objects of a subset of descendant VDAXs that participate in the digital enclave. In some embodiments, an enclave VDAX controls a digital enclave's genomic network topology without requiring modifications to the digital enclave's physical network topology. In some embodiments, a digital ecosystem includes multiple genomic topologies that overlay one or more physical network topologies, where the multiple genomic topologies exist simultaneously and interoperably. In some embodiments, an ecosystem VDAX builds and controls a genomic network topology that supports applications with dynamic state attributes.

[0043] In some embodiments, the system further comprises a set of one or more Enclave VDAXs, each Enclave VDAX corresponding to a respective digital enclave of the digital ecosystem and assigned a respective enclave-specific genomic dataset that controls a portion of the genomic network topology that is responsible for ecosystem-specific functions and processes of the digital ecosystem.

[0044] In some of these embodiments, the system further includes a set of Cohort VDAXs participating in the digital ecosystem, the set of Cohort VDAXs including one or more Cohorts each controlling a respective portion of the genomic network topology responsible for particular ecosystem-specific and / or enclave-specific functions and processes. In some of these embodiments, interactions by the set of Cohort VDAXs are controlled by respective Cohort Genomic Data Sets assigned to each Cohort VDAX in the set of Cohort VDAXs.

[0045] In some embodiments, the set of progeny VDAXs comprises a set of cohort VDAXs.

[0046] In some of these embodiments, the Cohort Genome Data Set for each Cohort of the set of Cohort VDAXs includes: one or more Cohort Genome Eligibility Objects including one or both of one unique CNA object and one unique PNA object; one or more Cohort Genome Correlation Objects including one or more LNA objects, each LNA object corresponding to a respective enclave into which the Cohort VDAX is admitted; and one or more Cohort Genome Differentiation Objects including one or more XNA objects, each XNA object corresponding to a respective enclave into which the Cohort VDAX is admitted.

[0047] In some of these embodiments, the digital ecosystem is a dynamic ecosystem, and the ecosystem security platform is configured according to a voluntary architecture that maintains its operational integrity regardless of how frequently one or more metric states of the dynamic ecosystem are updated.

[0048] In some of these embodiments, the ecosystem VDAX and descendant VDAXs collectively control the genome network topology in response to specific dynamic metric conditions. In some embodiments, the genome digital network topology is constructed to achieve a controlled level of interoperability. Some embodiments support multiple genome network topologies overlaid on a concurrent physical network topology.

[0049] In some embodiments, the ancestor genome differentiation object and each descendant genome differentiation object are ZNA objects. In some of these embodiments, the digital ecosystem is a virtual trusted execution domain implemented with respect to a device having one or more processors, and a descendant VDAX corresponds to each component of the device including at least one of one or more hardware components and one or more digital components, where the one or more hardware components include one of a system-on-chip (SoC), a core, or a disk, and the one or more digital components include one or more of an OS, a process, a thread, a library, or an application programming interface (API). In some of these embodiments, a descendant VDAX from the plurality of descendant VDAXs is configured to perform component binary isolation (CBI) with respect to the virtual trusted execution domain.

[0050] In some embodiments, the system further includes a set of descendant VDAXs, in these embodiments, each descendant VDAX configured with a respective descendant instance of the ecosystem security platform such that each descendant instance of the ecosystem security platform is configured with a respective set of functionally matching modules, each configured to perform one or more information-theoretic-accelerated computational complexity functions.

[0051] In some of these embodiments, the set of modules of each descendant instance of the ecosystem security platform includes a DNA module configured to manage a genomic dataset of the descendant VDAX and perform a set of genomic processes based on the genomic dataset. In some of these embodiments, the set of modules includes a link module configured to facilitate a secure exchange of a link with another VDAX to facilitate a unique, two-way symmetric engagement.

[0052] In some embodiments, the set of modules includes a sequence mapping module configured to process the sequence to derive a genomic engagement factor having a particular entropy, the genomic engagement factor being used to encode the digital object in a non-recurrent manner, the sequence being at least one of a public sequence or a private sequence. In some of these embodiments, the set of modules includes a binary conversion module configured to encode the digital object into a VBLS object based on the genomic engagement factors determined by the sequence mapping module, each VBLS object including the encoded digital object and metadata indicating the public or private sequence used to generate the respective genomic engagement factor used to encode the encoded digital object. In some of these embodiments, the binary conversion module is further configured to decode the received encoded digital object included in the received VBLS object based on the respective recreated genomic engagement factor.

[0053] In some embodiments, an ecosystem instance of the ecosystem security platform comprises a respective set of modules, each configured to perform a respective set of information-theoretic computationally complex functions. In some of these embodiments, each set of information-theoretic computationally complex functions is selected from cryptography-based functions, non-cryptography functions, and hybrid functions that include at least one stage performed using a cryptography-based function and at least one stage performed using a non-cryptography function. In some of these embodiments, the set of modules of the ecosystem instance includes a root DNA module that manages ancestral genomic datasets and generates descendant genomic datasets.

[0054] In some embodiments, the digital ecosystem is a cloud services system. In some embodiments, the digital ecosystem is an enterprise information technology system. In some embodiments, the digital ecosystem is a computing device, and the descendant VDAXs each correspond to a hardware component and a digital component of the computing device. In some embodiments, the digital ecosystem is a classified computing infrastructure. In some embodiments, the digital ecosystem is a traffic grid. In some of these embodiments, the traffic grid is an air traffic control grid. In some of these embodiments, the traffic grid is an autonomous vehicle traffic grid.

[0055] According to some embodiments of the present disclosure, a method for managing a set of digital entities in a digital ecosystem is disclosed. The method includes generating, by a processing system of an ecosystem VDAX, a progeny genomic dataset having a particular entropy, where the progeny genomic dataset is assigned to the ecosystem VDAX. The method further includes generating, by the processing system, a plurality of different progeny genomic datasets, each of the different progeny genomic datasets exhibiting a particular entropy. The method also includes, for each progeny of the plurality of different progeny genomic datasets, assigning, by the processing system, the progeny genomic dataset to a respective digital entity of the set of digital entities, where the set of digital entities is capable of achieving precise control of differences and correlations based on the respective progeny genomic datasets of each digital entity.

[0056] In some embodiments, any pair of digital entities in the set of digital entities is configured to verify a correlation of their respective genomic datasets and distinguish each progeny genomic dataset from any other progeny genomic dataset based on the verified correlation of the progeny genomic datasets of the pair of digital entities to form a unique non-recursive relationship within the digital community. In some of these embodiments, each pair of digital entities is configured to independently verify the correlation using a particular set of information-theoretically accelerated computational complexity functions.

[0057] In some embodiments, the set of information-theoretically facilitated computational complexity functions is one of cryptography-based functions, cryptography-free functions, and hybrid functions including at least one cryptography-based function and at least one cryptography-free function. In some embodiments, each digital entity in the set of digital entities is configured to independently distinguish its respective progeny genomic data set using a second set of information-theoretically facilitated computational complexity functions.

[0058] In some of these embodiments, the second set of computationally complex functions is one of cryptography-based functions, non-cryptography functions, and hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

[0059] In some embodiments, in response to forming a unique acyclic relationship, the pair of entities engage by generating and exchanging a unique acyclic virtual binary language script (VBLS) decodable only by the pair of entities based on differentiated progeny genomic data. In some of these embodiments, the VBLS is comprised of an encoded digital object that holds information-theoretically driven genomic attributes of the genomic datasets of each of the pair of digital entities.

[0060] In some embodiments, the specified entropy is a configurable level of entropy defined by a community owner associated with the ecosystem VDAX. In some embodiments, the digital entities collectively enable virtual authentication, virtual affiliation, and virtual agility.

[0061] In some embodiments, the progeny genomic dataset includes a genomic correlation object that exhibits a particular entropy and a genomic differentiation object that exhibits a particular entropy. In some of these embodiments, each progeny genomic dataset includes a respective genomic eligibility object that exhibits a particular entropy.

[0062] In some embodiments, the digital ecosystem includes one or more digital enclaves formed around respective mutual interests expressed by controlled differences and correlations in the progeny genomic dataset. In some of these embodiments, each digital enclave includes one or more cohorts that share respective mutual interests expressed by controlled differences and correlations in the progeny genomic dataset.

[0063] A more complete understanding of the present disclosure will be obtained from the following description and accompanying drawings, and the claims.

[0064] The accompanying drawings, which are included to provide a better understanding of the present disclosure, illustrate embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure. [Brief explanation of the drawings]

[0065] [Figure 1] FIG. 1 is a schematic diagram illustrating features of a cyphergenic-based digital ecosystem in relation to an organic ecosystem and a modern digital ecosystem, according to some embodiments of the present disclosure.

[0066] [Figure 2]FIG. 2 illustrates an example of a CipherGenics-enabled security stack that may be applied in coexistence with commonly known application and network stacks, in accordance with some embodiments of the present disclosure, and a CipherGenics-enabled genomic architecture of a digital ecosystem that may result from such application.

[0067] [Figure 3] FIG. 3 illustrates attributes of Ciphergenics-based technology in the context of attributes (and potential drawbacks) of current and developing security-related technologies, according to some embodiments of the present disclosure.

[0068] [Figure 4] FIG. 4 illustrates an example configuration of a CipherGenics-based ecosystem security platform, according to some embodiments of the present disclosure.

[0069] [Figure 5] FIG. 5 is a diagram illustrating an exemplary implementation of a genomic dataset, according to some embodiments of the present disclosure.

[0070] [Figure 6] FIG. 6 illustrates an example of a CipherGenics-enabled digital ecosystem managed by a set of CG-enabled VDAXs, according to some embodiments of the present disclosure.

[0071] [Figure 7] FIG. 7 illustrates an example implementation of a security platform instance configured according to a directed architecture supporting a static ecosystem, in accordance with some embodiments of the present disclosure.

[0072] [Figure 8] FIG. 8 illustrates an example implementation of a security platform instance configured according to a free-form architecture that supports a transitional ecosystem, in accordance with some embodiments of the present disclosure.

[0073] [Figure 9] FIG. 9 illustrates an example implementation of a security platform instance configured according to a voluntary architecture that supports a real-time / dynamic ecosystem, in accordance with some embodiments of the present disclosure.

[0074] [Figure 10] FIG. 10 illustrates an example implementation of a security platform instance configured according to a transient architecture supporting virtual trusted execution domains, in accordance with some embodiments of the present disclosure.

[0075] [Figure 11] FIG. 11 illustrates an example implementation of a process for forming a unique non-recursive engagement to exchange data and securely exchanging data based on the unique non-recursive engagement, according to some embodiments of the present disclosure.

[0076] [Figure 12] 12 and 13 show examples of different CG-enabled digital ecosystems that may be formed in accordance with some embodiments of the present disclosure. [Figure 13] 12 and 13 show examples of different CG-enabled digital ecosystems that may be formed in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0077] It is submitted that hyperscalability (e.g., the ability of N digital populations to directly establish mutual identity of interest with N different digital populations exhibiting high entropy) is critical to all things network-centric and is the missing link to comprehensive security for dynamic state virtualization of digital ecosystems. Hyperscalability (e.g., many-to-many) has the potential to facilitate entirely new network-centric virtualization products and services in addition to comprehensive security.

[0078] As described herein, the hyper-scalability enabled by CipherGenics requires minimal additional overhead or bandwidth, and is equally effective and computationally robust across all network and application stack levels, even though NxN engagement instances may increase to NxxNY. In embodiments, the results of hyper-scalable direct digital cohort-to-cohort virtual authentication and virtual affiliation are similar to the results of hyper-scalable biological gene-to-gene virtual correlation and virtual differentiation, even though the underlying technologies on which each is achieved are very different. The same can be said for other digital attributes enabled by CG hyperscalability (e.g., virtual agility, virtual data objects, virtual code objects).

[0079] The description of CipherGenics is substantially aided by the adoption of an accompanying genomic terminology that, while fully digitally realized, is fully biochemically realized, which is likely and hopefully more familiar to all but a select few at this time. While specific biochemical processes have not technically influenced CipherGenics, their ability to address similar challenges and levels of complexity has added a posteriori merit to the fundamental proposal of CipherGenics. Non-limiting examples of CipherGenics-based terminology and computationally complex digital and biochemical-based functions and processes, among others, that share correlated genomic representations include:

[0080] Genomic information: Numerical, narrative, or other such scripts whose elements, taken as a whole, exhibit little or no computationally separable order or relationship.

[0081] Genomic entropy: The degree to which genomic information lacks repeating or predictable patterns that can be assessed computationally.

[0082] Genome assembly: The ability to rearrange or reorganize genomic information into specific subsets from the original sequence or relational basis without losing relative entropy.

[0083] Genome modification: The ability to process genomic information based on computationally complex functions and processes, where the computational properties are provable, if not observable, and remain consistent across the modified genome information base.

[0084] Genomic modulation: The ability to conditionally and temporarily modify the complete genomic information base or specific subset(s) to achieve a specific purpose (e.g., digital cohort precision unadjusted relapse), requiring prior knowledge about the current base at that time (i.e., after modification).

[0085] Genome revision: The ability to derive a subset of genomic information by rearranging, modifying, or adjusting digital data objects and digital code objects.

[0086] This disclosure relates to various embodiments of the CipherGenics Ecosystem Security Platform (also referred to as the "CG-ESP," "security platform," or "genome security platform") and the processes and techniques it facilitates. In embodiments, the CG-ESP provides the computational resources and process control where genomic information, genomic entropy, genomic assembly, genomic modification, genomic regulation, and genomic revision uniquely collaborate to achieve computationally complex hyper-scalability. In embodiments, the hyper-scalability in turn enables digital ecosystems, enclaves, and digital cohorts to engage on a genomic basis to achieve virtual authentication, virtual affiliation, virtual agility, virtual sessionless engagement, and / or virtual execution domain attributes. In some embodiments, these attributes in turn facilitate application-specific genomic network security topologies without replacing or reconfiguring hardware connections.

[0087] In embodiments, an instance of CG-ESP may be parameterized with specific information-theoretically constructed genomic attributes (e.g., a digital genomic dataset reflecting one or more mutual identities of interest for a particular digital community comprised in whole or in part of an ecosystem, enclave, and / or digital cohort (or "cohort"). In embodiments, the parameterization of a CG-ESP instance with specific information-theoretically constructed genomic attributes constitutes a respective Virtual Anonymous Exchange Controller (or "VDAX"), which can be executed by a processing system to enable the VDAX to fulfill a role within the respective digital community. In embodiments, a CG-ESP can enable multiple VDAXs, regardless of whether they have differing or overlapping mutual identities of interest.

[0088] In embodiments, the CG-ESP may be configured to build and manage digital correlation and differentiation functions on behalf of a digital ecosystem or its components. In embodiments, a digital ecosystem may refer to a digital community having one or more enclaves each with a mutual identity of interest. In embodiments, an enclave may refer to a collection of one or more cohorts with a mutual identity of interest. In embodiments, the term "cohort" may refer to an independent cohort and / or a dependent cohort. In some embodiments, an independent cohort may refer to a collection of one or more devices operating as an independent entity. In some of these embodiments, an independent cohort may include, but is not limited to, a grid, a network, a cloud service, a system, a computer, an appliance, a device, and an IoT device. A dependent cohort may refer to an individual digital entity enabled by an independent cohort acting as a surrogate for the individual digital entity. Examples of dependent cohorts include, but are not limited to, sensors, applications (apps), data, files, and content. As described, the designations of independent and dependent cohorts may vary across different types of architectures and ecosystems. For example, according to some embodiments of the Transient Architecture (described below), certain device components (e.g., processors, processor cores, cameras, etc.) and software instances may be designated independent cohorts, while other device components and software instances may be designated dependent cohorts. Note that in some embodiments, these types of designations may be determined by community owners associated with the digital ecosystem.

[0089] In an exemplary embodiment, a CipherGenics-based Ecosystem Security Platform ("CG-ESP") forms an ecosystem with one or more enclaves and manages membership in a collection of independent and dependent cohorts with mutual identities of interest. In an embodiment, the CG-ESP provides one or more core capabilities, such as platform capabilities to control and manage genomic functions and processes, and link exchange capabilities that provide a means by which link data (e.g., genomic engagement cargo) is exchanged. In an embodiment, mutual identities of interest may be defined according to any logical commonality between cohorts within an enclave, which may be defined by or on behalf of a community owner. For example, mutual identities of interest may exist between user devices, servers, printers, documents, and applications (e.g., cohorts) that form business units (e.g., enclaves) within a corporate organization (e.g., a digital ecosystem). In another example, just as a home network has one or more enclaves (e.g., a work-related enclave used for the home office and a personal enclave for personal or family devices, applications, and files), mutual identities of interest may exist among user devices, smart devices, gaming devices, sensors, wearable devices, files, and applications (e.g., cohorts) operating on a home network (e.g., ecosystem). In another example, mutual identities of interest may exist among autonomous vehicles (e.g., cohorts) operating on a particular grid (e.g., enclave) of a smart transportation system (ecosystem) managed by a local authority (e.g., community owner). The above are non-limiting examples of ecosystems, enclaves, cohorts, and identities of interest; many other examples will be described throughout this document.Additionally, note that because digital entities may be considered a cohort in a first ecosystem (e.g., mobile devices in an enterprise ecosystem), digital entities may play different roles within or across ecosystems. For example, mobile devices in an enterprise ecosystem may be considered an enterprise ecosystem cohort, but may define the entire digital ecosystem within an executable ecosystem.

[0090] As will be described in more detail, the configuration of the CG-ESP may be defined by a community owner of a digital ecosystem. When referring to a "community owner" throughout this disclosure, the term may refer to an entity (e.g., a company, organization, government, individual, etc.) that manages, maintains, or owns the community and / or its representative (e.g., a network administrator, CIO, IT administrator, family member, consultant, security expert, artificial intelligence software acting on behalf of the community owner, or any other suitable representative). Furthermore, in some embodiments, the CG-ESP may be sold pre-configured to the community owner, thereby allowing the community owner to make decisions regarding community membership and / or the functionality of the CG-ESP (e.g., which CG-ESP modules and configurations to use in the CG-ESP).

[0091] In the context of biology, core biological genomic capabilities, including biological differentiation and correlation, provide convenient correlations for describing the formulation of CG-ESP digital processes. However, it will be understood that any reference to or derivation of a "genomic" cohort (e.g., genomic datasets, DNA, sequence mapping, mutation, cloning, and / or the like) in the context of CipherGenics-related technologies is not intended to suggest that these processes mimic or inherent any or all specific characteristics of biological genomic constructs or processes. In embodiments, CG-ESP performs genomic processes that may include the digital generation, modification, corroboration, and / or assignment of specific types of genomic data. In embodiments, these genomic processes and data enable the computation of differences and correlations that exhibit user-controlled entropy. In these embodiments, these digital genomic processes rely on specific information-theoretically driven constructs. In some embodiments, these constructs may be referred to as digital DNA (or genomic data). In embodiments, digital DNA may include one or more information-theoretically driven structures such as LNA (Genome Correlation), CNA (Genome Engagement-Completeness), PNA (Genome Engagement-Competence), XNA (Genome Differentiation), and / or ZNA (Genome Code Separation / Cloaking). As described, these cyphergenics-based (or "CG-based") processes and structures facilitate hyper-scalability across a broad range of digital ecosystems. Examples of CG-based processes may include, but are not limited to, CG-based linking processes, CG-based sequence mapping, and / or CG-based transformations, and example implementations of these are described throughout this disclosure.

[0092] In embodiments, genomic digital links (or "links") enable the exchange of information necessary for a Virtual Digital Anonymous Exchange Controller ("VDAX") (described further below) to perform higher-level, computationally complex genomic functions. In embodiments, CG-based genomic linking processes may include link generation, link hosting, and / or link updating, examples of which are described throughout this disclosure. These CG-based linking processes provide attribute-specific genome assembly information such as LNA (Genome Association), CNA (Genome Engagement-Completeness), and PNA (Genome Engagement-Quality).

[0093] In embodiments, CG-based sequence mapping may refer to the following techniques: computational conversion of digital sequences (e.g., public or private protocol sequences) into genome engagement factors. In embodiments, these genome engagement factors may be unique and non-repetitive. While different types of sequences may be widely heterogeneous, sequences may be processed in a way that results in genome engagement factors that exhibit a particular level of entropy. In embodiments, a CG-based sequence mapping process, compatible with a wide range of protocols and formats, may begin with sequences that exhibit existing entropy, whereby each sequence is transformed by a computationally complex CG-function and processed into a unique genome engagement factor. These genome engagement factors may be used to encode digital objects into VBLS.

[0094] As described, embodiments of CG-ESP may facilitate numerous hyper-scalable attributes not possible with modern cryptography and related security systems. These attributes may include, but are not limited to, virtual affiliation (infinite differences), virtual authentication (infinite correlations), virtual agility (infinite structural adaptability), and virtual engagement (sessionless control per discrete data object), and virtual trusted execution domains (execution control per discrete code object). It should be noted that the term "unlimited" as used herein means unlimited in all practical senses, while recognizing that it is theoretically possible to describe "bounded" scenarios.

[0095] Hyperscalability: In some embodiments, hyperscalability may refer to the ability to comprehensively associate N cohorts (or other community members) by M contact points across T instances (M x N x T). Considering billions of potential cohorts with countless contact points and communicating across countless instances, hyperscalability of this magnitude requires fundamental breakthroughs in modern cryptography. The CG-based systems described herein achieve significant reductions in computational expense and session state. In embodiments, these significant reductions come at the expense of relatively insignificant overhead and / or bandwidth.

[0096] Virtual Authentication. In embodiments, virtual authentication of ecosystem members (e.g., ecosystems, enclaves, cohorts, etc.) may require hyper-scalable technologies. In embodiments, hyper-scalable technologies enable ecosystem, enclave, and / or cohort engagement where precise and unique correlation (e.g., “who is who”) may be required. In some of these embodiments, precise and unique correlation may refer to a specific set of information-theoretically facilitated genomic processes in which a digital community (e.g., cohort, enclave, ecosystem, etc.) uniquely verifies the identity of another member (e.g., another cohort, enclave, ecosystem, etc.). In embodiments, virtual authentication may refer to the ability to authenticate an infinite number of ecosystem members (e.g., ecosystems, enclaves, cohorts, etc.). As discussed, a CG-enabled ecosystem may achieve infinite correlation for ecosystem members (e.g., enclaves, cohorts, subordinate cohorts), which in turn provides an infinite amount of unique relationships to be formed. In some embodiments of the present disclosure, cohorts from different ecosystems may also be configured to authenticate with each other in an unlimited manner. As described, unlimited correlation may be achieved through genomic information theory-driven architectures and processes (also referred to as "CypherGenics-based" or "CG-based" or "CG-enabled" architectures and / or processes).

[0097] Virtual Affiliation: In embodiments, virtual differentiated engagement between ecosystems, enclaves, and cohorts may require hyperscalability. Hyperscalable technologies enable ecosystem, enclave, and cohort engagement for most, if not all, scenarios where precise and unique differentiation (the “what” and the “only us”) may be required. In embodiments, precise and unique differentiation may refer to a matched or sufficiently matched set of processes performed by a unique pair of community members to establish a unique engagement that differentiates the pair from any other community member. In some of these embodiments, such precise and unique differentiation ensures that unintended digital entities cannot participate in the uniquely established engagement (e.g., decrypt intercepted data, etc.). In embodiments, hyperscalable differentiation may refer to the ability of ecosystem members to uniquely affiliate with an infinite number of other ecosystem members (e.g., ecosystems, enclaves, cohorts, and / or the like). Organic ecosystems have demonstrated strong, albeit limited, differentiation between species, offspring, and siblings resulting from complex biochemical processes. However, boundless differentiation may be possible through digital construction and processing based on genomic information theory. As described, CG-enabled ecosystems may achieve unlimited differentiation for ecosystem members (e.g., enclaves, cohorts, subordinate cohorts), which in turn provides an unlimited amount of unique relationships to be formed. In some embodiments of the present disclosure, cohorts from different ecosystems may also be configured to form unique interactions in unlimited ways. As described, unlimited differentiation may be achieved through construction and processes governed by genomic information theory (also referred to as "cyphergenics-based" or "CG-based" constructions and / or processes).

[0098] Virtual Agility: In embodiments, virtual agility within ecosystem, enclave, and / or cohort platform stack(s) may be enhanced by hyperscalability. Hyperscalability techniques enable ecosystems, enclaves, and cohorts to agilely execute hyperscalable differentiation and correlation of software and hardware-managed processes. In some embodiments, agile execution of software and / or hardware-managed processes may refer to processes applicable at various levels of the respective protocol stacks (e.g., OSI-network stack, software stack, processing stack, and / or the like). Organic ecosystems demonstrate limited but powerful agility at the cellular level, controlled by complex biochemical processes. However, unlimited agility may be achieved through digital constructs and processes based on specific genomic information theory.

[0099] Virtual Binary Language Script (VBLS): Engagement of ecosystems, enclaves, and / or cohorts enabled by Virtual Binary Language Script requires hyperscalability. Hyperscalable technologies can enable ecosystems, enclaves, and cohorts to engage via unique, non-recurring, computationally quantum-proof binary languages ​​(or non-quantum-proof binary languages, if the community owners so desire). Organic ecosystems demonstrate powerful, limited, and unique cell-to-cell interactions based on complex biochemical processes. However, unlimited unique digital object engagement may be achieved through digital structures based on specific genomic information theory.

[0100] Virtual Trust Execution Domain: In some embodiments, employing computationally complex genomic architectures and processes, a suitably configured CG-ESP enables processes for uniquely transforming engagements for components of an executable ecosystem. In embodiments, an executable ecosystem may refer to different software and hardware components of a device (or a system of interdependent devices operating as a single unit). In embodiments, components of an executable ecosystem may include, but are not limited to, application components (APIs, libraries, threads), operating system components (e.g., kernels, services, drivers, libraries, etc.), system-on-chip (SOC) components (processing units, e.g., cores), hardware components (e.g., disks, sensors, peripheral devices, and / or the like), and / or other suitable types of components. In some embodiments, these ecosystem components (e.g., specific designations and organizations such as ecosystems, enclaves, and cohorts) may jointly or independently process (e.g., encode and / or decode) executable binaries.

[0101] Genomically Driven Virtual Network Architecture: In embodiments, the genomic processes and capabilities of CG-EPS enable the inversion of application security and network architecture relationship protocols, regardless of the unique requirements of a particular use case. In some embodiments, the disclosed "genomic network topology" technology enables the creation of entirely new, use-case-specific security architectures. In some embodiments, a single physical network topology can simultaneously support multiple genomic topologies. As used herein, genomic topology or genomic network topology may refer to the topology of a digital ecosystem defined using the genomic structure of each member of the digital ecosystem.

[0102] In embodiments, ecosystems, enclaves, and their membership may be defined by a digital community owner. For example, a network administrator affiliated with a corporate entity may configure a security platform instance to establish respective enclaves for different units or projects of the corporate entity. In this example, the network administrator may configure the security platform instance to add cohorts to one or more enclaves based on the cohorts' functions. Note that in some embodiments, a cohort may be included in multiple enclaves, and enclaves may have overlapping cohorts. Furthermore, in some embodiments, multiple cohorts may be associated with a single device, such as a computing device and various hardware (e.g., CPU, GPU, memory device) and / or software components (operating system, file system, applications, files). As described, a CG-ESP may be configured to form many different types of ecosystems, and membership may be configurable and defined by the community owner and / or CG-ESP provider. In embodiments, the security platform is configured to "genomically" structure heterogeneous functions, systems, and / or arenas of operation based on mutual identity of interest. Stated another way, in these embodiments, the CG-ESP may be configured and operated (e.g., by a community owner or similar party) to control the genome network topology of the digital ecosystem using the genome construction of community members (e.g., enclaves, ecosystems, and / or cohorts) within the digital community. In this manner, community members can be established, added to, revoked from, and so forth, by modifying the genome construction of one or more members within the digital community.

[0103] In some embodiments, a CipherGenics-Based Ecosystem Security Platform (CG-ESP) may refer to a set of CG-enabled modules that perform various CG-based functions on a particular configuration of genomic data, and a CG-ESP instance may refer to an instance of a CG-ESP platform with a configuration of CG-enabled modules depending on the role the CG-ESP instance is performing with respect to the community (e.g., ecosystem level, enclave level, cohort level, subordinate cohort level). In some embodiments, a CG-ESP platform instance may be embodied as a VDAX. In these embodiments, a VDAX may perform a particular configuration of CG-enabled modules defined for the VDAX role. Examples of VDAX roles may include an ecosystem VDAX, an enclave VDAX, a cohort VDAX, and / or a subordinate VDAX, whereby each of these VDAXs may be configured according to the CG-enabled operations required by the CG-ESP modules and roles. In embodiments, a CG-ESP instance may provide one or more core capabilities, which may include control and management of genome architecture, functions, and processes (platform capabilities) and / or secure data exchange functions and processes (link exchange capabilities). In embodiments, an ecosystem VDAX may perform security-related functions on behalf of the ecosystem and may be considered the "ancestor" of the ecosystem. In some of these embodiments, one or more corresponding enclave VDAXs may be configured to perform security-related functions on behalf of their respective enclaves. In embodiments, a cohort VDAX may perform genome security-related functions on behalf of each independent cohort in the ecosystem. In embodiments, a subordinate VDAX may perform genome security-related functions on behalf of each subordinate cohort in the ecosystem. Note that in some embodiments, an independent cohort may host one or more subordinate VDAXs on behalf of one or more subordinate cohorts that depend on the independent cohort.Additionally, in some embodiments, a single Cohort VDAX associated with an independent cohort may be configured to perform security-related functions for the cohort across disparate enclaves and ecosystems. In these embodiments, the Cohort VDAX may manage and leverage different genomic datasets on behalf of the cohort according to the respective configurations of the different ecosystems and / or enclaves. For example, a mobile device used by a user for work and personal matters may be configured with a Cohort VDAX that manages and leverages one or more genomic datasets associated with the user's work ecosystem and enclaves according to the CG-ESP configuration of the organization where the user works, as well as one or more genomic datasets associated with the ecosystems and enclaves in which the user participates according to the platform configurations of those ecosystems and enclaves. In some embodiments, one or more Enclave VDAXs and / or Ecosystem VDAXs may be hosted by the same computing system. For example, the ecosystem and enclave VDAX of a large digital ecosystem (e.g., federal or state government, large enterprise, military, autonomous vehicle grid, IoT grid, etc.) may be a distributed cloud computing system (e.g., AWS, Azure, Google Cloud Services, private server bank, etc.), while the ecosystem and enclave security controller of a small digital ecosystem (e.g., home network, small office network, grassroots nonprofit, etc.) may be hosted on a single computing device (e.g., central server, router, mobile device, etc.). Also, note that in some example embodiments, the VDAX may be configured to play different roles with respect to different ecosystems.

[0104] For purposes of explanation, the terms "ancestor" and "descendant" (e.g., "ancestor security controller" or "ancestor VDAX" and "descendant security controller" or "descendant VDAX") may be used to indicate a relationship in which an "ancestor" VDAX may generate, assign, and / or otherwise provide genomic datasets to "descendant" VDAXs. For example, in some embodiments, an ecosystem VDAX may modify its own digital genomic dataset for one or more enclaves such that, for each enclave, its "descendant" enclave VDAX is assigned a unique but correlated genomic dataset derived from the "ancestor" ecosystem VDAX. Similarly, in another example, an enclave VDAX may modify its own genomic dataset to generate, assign, and / or otherwise provide a unique but correlated genomic dataset for a cohort within the enclave such that, for each enclave, its descendant cohort VDAX is assigned its own genomic dataset. In this way, descendants (e.g., descendant VDAXs) of an ancestor VDAX may be able to exchange data in a cryptographically secure manner, in part due to the high correlation between their respective genomic datasets. Note that in some embodiments of the CG-ESP platform, an ecosystem VDAX may generate and allocate genomic datasets for a cohort of the ecosystem, even in the presence of an enclave VDAX. In embodiments, an ancestor VDAX (e.g., an ecosystem or enclave VDAX) may provide a role-based configuration of the CG-ESP platform to a descendant VDAX, such that the descendant VDAX is configured with appropriate CG-ESP modules that are assigned the descendant VDAX's role with respect to the VDAX. As described, the configuration may include respective modules configured with specific cryptographic-based, cryptographic-free, and / or hybrid computational complexity functions used in the described CG-ESP process. In embodiments, a cryptographic-based function may refer to a function executed in which all stages (one or more stages) of the function are performed using a key-based reversible transformation (e.g., a symmetric cipher).Examples of key-based reversible functions include, but are not limited to, Advanced Encryption Standard (AES), SAFER+, Serpent, Twofish, RC6, MARS, Camelia, MISTY!, SHACAL-2, Triple-DES, SAFER++, HC-138, Rabbit, Sasa20 / 12, SOSEMANUK, Grain, MICKEY, Trivium, and / or other suitable key-based reversible functions now known or later developed. In embodiments, a cipherless function may refer to a performed function in which no stage of the function is performed using a key-based reversible function. In embodiments, a hybrid function may refer to a performed function that includes at least one stage performed using a cipher-based function and at least one stage performed using a cipher-less function. For example, a hybrid function may include a first stage in which a cipher-based function is used to determine an intermediate value and a second stage in which a cipherless function converts the intermediate value to an output value, which may or may not be reversible.

[0105] In some embodiments, the CG-ESP is configurable by the ecosystem owner or an agent of the ecosystem owner. As previously mentioned, the CG-ESP may include a set of interdependent modules that collectively perform one or more genomic security functions, such that any level VDAX includes instances of some or all of the interdependent modules. It is noted that these interdependent modules may be implemented as executable instructions executed by a conventional processing device (e.g., a CPU or GPU and / or FPGA, microprocessor, or special-purpose chipset) specifically configured to perform a particular genomic function. In other words, the interdependent modules of a particular security controller instance (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, a subordinate VDAX, and / or the like) may be individually embodied as software, middleware, firmware, and / or hardware. References to processor, execution, or the like are intended to apply to any of these configurations unless the context specifically dictates otherwise. In embodiments, individual modules of a particular CG-ESP instance may be configured to operate on a particular set of different types of genomic data objects (e.g., CNA, LNA, XNA, PNA, ZNA, etc.) and / or to perform different types of functions and strategies. For example, some modules may be configured to apply cryptography-based computational complexity functions to genomic data objects and / or digital data generated or utilized in connection with genome security operations. Examples of cryptography-based computational complexity functions include, but are not limited to, Advanced Encryption Standard (AES) encryption / decryption, SAFER+ encryption / decryption operations, proprietary, privately developed encryption / decryption operations, and / or the like. Additionally or alternatively, in some embodiments, some of the interdependent modules may be configured to apply cryptography-less computational complexity functions to genomic data objects and / or digital data generated or utilized in connection with genome security operations.Examples of cryptography-less computational complexity functions may include, but are not limited to, cryptographic hash functions, transforms based on parametric linear equations, transforms based on multivariate equations, lattice-based transforms, etc. Additionally or alternatively, in some embodiments, some modules may be configured to apply hybrid (e.g., cryptography-based and cryptography-less) computational complexity functions to genomic data objects and / or digital data generated or utilized in connection with genome security operations. As described, hybrid functions may include any combination of cryptography-based and cryptography-less functions. As described, the CG-ESP may be configured (e.g., by or on behalf of a community owner) according to the needs and constraints of the digital community it serves.

[0106] In embodiments, CG-ESP is configured to perform secure end-to-end data exchange between ecosystem members using specific genomic datasets containing one or more digitally generated genomic structures that may be embodied in objects exhibiting configurable entropy (e.g., binary matrices, binary vectors, primitive binary polynomials, etc.). In embodiments, these digitally generated mathematical objects are used to securely exchange digital objects between any pair of sufficiently correlated ecosystem members by leveraging the high correlation and differentiability between the ecosystem members' respective genomic datasets using a series of CG-based processes. In embodiments, hyperscalable genomic correlation may provide the ability to have an infinite community of genomic descendants that can be directly authenticated as fellow enclaves or ecosystem members without the support of an out-of-bounds trusted service (e.g., certificate authority, private key exchange, etc.). In some embodiments, hyperscalable differentiation may refer to the ability of two well-connected cohorts to generate and exchange virtual binary language scripts (VBLS) (individual instances of which may be referred to as VBLS objects) based on links hosted with other community members. In an embodiment, the link includes digitally encoded instructions (which may be referred to as "genomic regulation instructions" or "GRI") from one VDAX to another VDAX that define how the second VDAX's genomic dataset (e.g., XNA or ZNA) should be modified in the second VDAX to generate a VBLS that can be decoded by the VDAX (assuming the link is securely maintained by the second VDAX). In an embodiment, the second VDAX may "host" a link that indicates a GRI corresponding to the first VDAX, whereby the second VDAX may modify its genomic dataset based on the GRI and generate a VBLS that is readable by the first cohort based on the modified genomic data.In embodiments, the second VDAX maps the sequence to the corrected genomic data to obtain a genomic engagement factor, which is then used to encode (e.g., using disambiguation and / or encryption techniques) a digital object included in the VBLS object. In embodiments, the VBLS object may be a data container that includes one or more encoded digital objects and metadata used to decode the encoded digital object(s). In embodiments, the first VDAX receives the VBLS object, corrects its own genomic dataset using the GRI provided to the second VDAX, determines a genomic engagement factor based on the corrected genomic dataset, and decodes the encoded digital object based on the genomic engagement factor, thereby decoding the encoded digital object. As described below, only the first VDAX can decode the digital object from the VBLS; other cohorts (digital community members or others) who do not have access to the GRI included in the linking information cannot decode the encoded digital object in the VBLS.

[0107] As described, in embodiments, CG domain components (e.g., ecosystems, enclaves, independent cohorts, and / or dependent cohorts) may be configured with digital genome datasets such that digital ecosystem CG-enabled components may achieve precise control of differences and correlations using information-theoretic-facilitated computational complexity functions, which may be cryptography-based, cryptography-free, and / or hybrid functions. In embodiments, CG-enabled methods may support CG-based genomic processes that enable dynamic specification of entropy. In embodiments, CG domain components may engage according to information-theoretic genomics (ciphergenics) and utilize information-theoretic genomics capable of virtual authentication, virtual scalability, and / or virtual agility. In embodiments, CG components are configured to generate a unique, non-regenerative digital language (e.g., VBLS) such that CG-enabled processes allow two domain components to construct and engage via the VBLS. In embodiments, CG components provide the ability to establish and control differences and correlations between CG domain components, which may enable extensive scalability (e.g., hyper-scalability). In embodiments, CG domain components engage through specific digital protocols configured in digital objects, whereby the digital objects retain the information genome attributes of their descendent components (e.g., ecosystems or enclaves). In embodiments, when exercised at the digital object level, hyper-scalability enables agile application of CG-based attributes beyond the component level (e.g., at the format and / or protocol level).

[0108] As previously mentioned, a genomic dataset (also referred to as a "digital DNA set," "DNA set," or "DNA") may include one or more digitally generated mathematical constructs that exhibit a particular level of entropy, such that the level of entropy is configurable. As previously mentioned, for purposes of explanation, references and derivations to biological genetic concepts are made. For example, terms such as DNA, "mutation," "genomic data," "genomic construct," "progeny," "cloning," and "sequence mapping" are used throughout this disclosure. It should be understood that such references are not intended to attribute specific properties of biological genetic material or processes to any of the terms used herein. Rather, the terms are used to teach others how to practice various aspects of the present disclosure. In embodiments, a genomic dataset may include a genomic eligibility object, a genomic correlation object, and / or a genomic differentiation object.

[0109] In embodiments, a genomic eligibility object may refer to a digitally generated mathematical object that allows a pair of cohorts to genomically confirm engagement eligibility, which may be performed as part of a "trustless" authentication process between two VDAXs. In embodiments, a descendant VDAX (e.g., an ecosystem VDAX) may derive a genomic eligibility object for its descendants from its genomic eligibility object ("descendant genomic eligibility object"), such that each descendant receives a unique but correlated genomic eligibility object. Once assigned a genomic eligibility object, the descendant VDAX may receive the genomic eligibility object. In some embodiments, the descendant VDAX may receive genomic eligibility via a one-time trusted event (e.g., upon ecosystem entry into a particular ecosystem, when a device is manufactured, configured, or sold, etc.). After this one-time trusted event, sufficient VDAXs can independently confirm engagement eligibility with each other using their respective genomic eligibility objects. In embodiments, the genomic eligibility objects may include CNA objects, PNA objects, or other suitable types of mathematical objects exhibiting configurable entropy levels, correlations, and derivatives, which are described in further detail below.

[0110] In embodiments, a genomic correlation object may refer to a digitally generated mathematical object that allows VDAXs to exchange links, whereby the links provide instructions that allow pairs of well-correlated VDAXs to be sufficiently distinguishable from other well-correlated VDAXs in the digital community. In embodiments, the genomic correlation object is used by VDAXs to verify link exchange correlations, thereby enabling two ecosystem components (e.g., enclaves and / or cohorts) to establish specific relationships and engage with one another. In an exemplary implementation, the genomic correlation object of a community member of a digital ecosystem is an LNA object or any other suitable type of mathematical object that exhibits configurable entropy and correlation, as described in further detail below.

[0111] In embodiments, a genome differentiation object may refer to a digitally generated mathematical object that enables a pair of VDAXs (e.g., enclaves or cohorts) to generate and decode VBLS objects generated by the other VDAXs when the VDAXs successfully host links generated by the other VDAXs. In some embodiments, a first VDAX generates VBLS for a second VDAX in part by modifying its genome differentiation object in a manner defined by instructions contained in a hosted link corresponding to the second VDAX, and decodes VBLS from the second VDAX in part by modifying its genome differentiation object in a manner defined by instructions contained in a hosted link by the second VDAX with respect to the first VDAX. Examples of genome differentiation objects may include, but are not limited to, XNA objects, ZNA objects, or any other suitable type of mathematical object exhibiting configurable entropy and correlation, as discussed in more detail below.

[0112] As will be described, different combinations and configurations of CG-ESP modules and genome datasets can be used in different CG-ESPs. Modern network capabilities substantially reflect their underlying deployment architectures. A VBLS-enabled genome construction architecture, operating at the bit level, can maintain interoperability with the underlying deployment architecture. According to embodiments of the present disclosure, VBLS provides unprecedented facilities and flexibility for uniquely tailored use cases, whether they are network-, software-, or hardware-centric. Examples of different architectures include directed architectures that can be deployed in static ecosystems (e.g., large enterprises), free-form architectures that can be deployed in transient ecosystems (e.g., social networks, websites), ephemeral architectures that can be implemented for dynamic ecosystems (e.g., city-wide autonomous vehicle control systems), executable ecosystems (e.g., operating systems, browsers), and / or interledger architectures that can be implemented for affirmative ecosystems (e.g., blockchains or other distributed ledgers). In embodiments, these architectures overlay existing physical network topologies to provide proof of concept genome construction topologies; multiple genome construction topologies may exist simultaneously and interoperably. Examples of different architectures and CG-enabled ecosystems are described throughout this disclosure.

[0113] 4 illustrates an example of a CG-ESP 400 according to some embodiments of the present disclosure. In these embodiments, the CG-ESP 400 includes a set of CG-modules configured to execute a set of CG-processes and associated computational methods with respect to a particular configuration of a genomic dataset, such that different CG-ESPs 400 may include different CG-modules that execute different genomic functions and associated computational methods operating on corresponding configurations of the genomic dataset. In embodiments, the CG-ESP 400 is configured to be executed by ecosystem members with different roles (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and / or dependent VDAX), such that the different roles may execute some, all, or all of the CG-processes and computational methods defined in each CG module of the CG-ESP. In this way, all community members may participate in a CG-enabled digital ecosystem using corresponding CG-ESP instances executed by and / or on behalf of the community member (e.g., for subordinate cohorts), such that the CG-ESP instance is configured for the community member's role (e.g., ecosystem, enclave, cohort, or subordinate cohort). For example, a community member functioning as an ecosystem ancestor (e.g., Ecosystem VDAX) may be configured with a CG-ESP instance including CG modules that define CG functions and associated computational methods for generating genomic datasets (digital DNA sets) having one or more specific types of genomic data (e.g., digital DNA sets). Meanwhile, a community member that is an independent cohort within the ecosystem (e.g., Principle VDAX) may be configured with CG modules that define CG processes and computational methods for mutating, linking, exchanging, sequence mapping, transforming digital objects, etc., its genomic data.In this manner, different community members of a CG-enabled ecosystem may execute different instances of a given CG-ESP 400. Note that in some embodiments, the modules of an instance of CG-ESP 400 are executed by a VDAX of the digital ecosystem that the CG-ESP instance supports. Further, because different types of VDAXs within a particular digital ecosystem may fulfill different roles within the digital ecosystem, it is noted that different classes of VDAXs of a CG-enabled ecosystem may execute some or all of the modules of a CG-ESP, and with respect to individual modules, different classes of VDAXs of a CG-ESP instance may be configured to perform some or all of the genomic functions of a CG module. Additionally, while different classes of VDAXs within a digital ecosystem may be configured to fulfill different respective roles within the digital ecosystem, all VDAXs configured to perform a certain CG-process with respect to the digital ecosystem (e.g., XNA and LNA modification, link exchange processing, VBLS generation / decoding, and / or the same) are configured with CG-ESP modules that contain functionally identical functionality to perform the particular CG-process (e.g., the same crypto-based, crypto-less, or hybrid functionality, the same sequence extraction functionality, etc.). In this manner, sufficiently related VDAXs can perform a particular CG-processing in a functionally consistent manner, such that sufficiently related VDAXs can, for example, verify engagement eligibility and / or integrity, create and host links, and create and decode VBLS objects.

[0114] In embodiments, the CG-modules of CG-ESP 400 may include a root DNA module 410, an executable isolation component (EIC) module 420, a link module 430, a sequence mapping module 440, a binary conversion module 450, an authentication module 460, and / or a master integrity controller 470. As previously mentioned, in some embodiments, a CG-enabled digital ecosystem includes a set of VDAXs, whereby the set of VDAXs includes two or more classes of VDAXs (e.g., ecosystem VDAX(s), enclave VDAX(s), cohort VDAX(s), and / or dependent VDAX(s)). In some of these embodiments, each class of VDAX may execute respective instances of some or all of the CG-ESP modules (e.g., root DNA module 410, EIC module 420, link module 430, sequence mapping module 440, binary conversion module 450, authentication module 460, and / or master integrity controller 470). In embodiments, individual modules may be cryptographic-based, cryptographic-less, and / or hybrid (eg, including functionality that is cryptographic-based and cryptographic-less).

[0115] In embodiments, each CG-ESP instance may be executed by a respective processing system, which may include one or more CPUs, GPUs, microcontrollers, FPGAs, microprocessors, special purpose hardware, and / or the like. Additionally, in some embodiments, modules of a CG-ESP instance may be executed by or within a virtual machine or container (e.g., a Docker container).

[0116] In embodiments, CG-ESP 400 includes a root DNA module 410. In embodiments, root DNA module 410 manages ecosystem-specific data and genomic processes, from which root DNA module 410 forms genomic constructs (e.g., DNA sets) that enable specific and highly precise differentiation and correlation. In some embodiments, root DNA module 410 may include a CNA module 412, a PNA module 414, an LNA module 416, and / or an XNA module 418.

[0117] In embodiments, the root DNA module 410 manages ecosystem-specific data and CG-genome processes, from which the root DNA module 410 creates CNAs that enable specific and highly rigorous differentiation and correlation. In embodiments, CG-enabled ecosystem component eligibility-correlation is enabled by the CG-genome process, which formulates and constructs CNA objects. In embodiments, the CNA module 412 may define CG-genome processes and associated methods configured to establish specific relationships between individual ecosystem components (ecosystems, enclaves, cohorts, and / or subordinate cohorts). In embodiments, a CNA may enable VDAXs of the same ecosystem to confirm eligibility for engagement. In embodiments, a CNA enables ecosystem VDAXs and sub-ecosystem VDAXs to uniquely maintain confirmation of eligibility for engagement. In embodiments, the CNA module 412 may be configured to perform genome-based eligibility correlation using a wide range of information-theoretically accelerated computational complexity functions. In embodiments, these information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0118] In embodiments, the root DNA module 410 manages ecosystem-specific data and CG genome processes, from which the root DNA module 410 forms a PNA that enables specific and highly rigorous differentiation and correlation. In embodiments, CG-enabled ecosystem component eligibility synchronization is enabled by the CG genome process, which formulates and constructs PNA objects. In some embodiments, the PNA module 414 defines a CG-process that employs the CG-genome process and associated computational methods to establish specific relationships between individual ecosystem components. In this manner, the PNA may enable ecosystem components (e.g., enclaves, cohorts, and / or subordinate cohorts) of the same ecosystem to confirm eligibility for engagement. In embodiments, the PNA allows sub-ecosystems of ecosystem VDAX and descendant VDAXs to nonetheless retain unique confirmation of eligibility for engagement. In embodiments, the PNA root module 414 may be configured to perform genome-based eligibility synchronization, which may be computed according to a wide range of information-theoretically driven computational complexity functions. In embodiments, these information-theoretically accelerated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

[0119] In embodiments, the root DNA module 410 manages ecosystem-specific data and CG genome processes, from which the root DNA module 410 creates LNAs that enable specific and highly rigorous differentiation and correlation. In some embodiments, CG ecosystem component link exchange-correlation is enabled by the CG genome process, which formulates and builds LNA objects. In embodiments, the LNA module 416 defines CG processes and associated computational methods to establish specific relationships between individual ecosystem components. In this way, LNAs may enable specific VDAXs within an ecosystem (e.g., members of the same enclave) to ascertain link exchange-correlation relationships. In embodiments, LNAs enable VDAXs within a digital ecosystem to exchange information ("link exchanges") that enable each to engage with the other, whereby link exchanges incur corresponding computational complexity. In some embodiments, link exchanges enabled by CG-based LNAs presuppose two sets of information, each unique to one of the parties (e.g., a first VDAX and a second VDAX), such that link exchanges between the parties are unique (e.g., bi-symmetric). In embodiments, the LNA route module 416 searches for genome-based link exchange-correlation, which can be computed according to a wide range of information-theoretically accelerated computational complexity functions. In embodiments, these information-theoretically accelerated functions can be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0120] In embodiments, the root DNA module 410 manages ecosystem-specific data and CG genome processes, from which the root DNA module 410 creates the specific and highly rigorous differentiation that makes XNA possible. In these embodiments, engagement-differentiation of ecosystem members may be enabled by the CG genome processes that formulate and construct XNA objects. In embodiments, the root XNA module 418 employs XNA-specific CG processes and associated computational methods to establish specific relationships between individual ecosystem components. In embodiments, XNA enables VDAXs of the same ecosystem to ascertain engagement differentiation. In some embodiments, XNA enables VDAXs of different ecosystems (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or dependent VDAXs) to ascertain engagement-differentiation. Engagement-differentiation allows pairs of VDAXs to sufficiently differentiate from other sufficiently correlated VDAXs for the purpose of securely exchanging data, thereby reducing the corresponding computational complexity of engagement. In some embodiments, XNA-enabled engagement may presuppose two sets of information, each unique to one of the parties, such that an engagement between two VDAXs (e.g., a first VDAX and a second VDAX) is unique (e.g., bisymmetric). In embodiments, the XNA module 418 performs a genome-based engagement differentiation, which may be computed according to a wide range of information-theoretically accelerated computational complexity functions. In embodiments, the information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0121] In embodiments, the CG-ESP may include an EIC module 420 that manages ecosystem-specific data and CG-genome processes, from which the EIC module 420 formulates a specific and highly rigorous differentiation structure called a ZNA. In embodiments, ecosystem EIC engagement differentiation is enabled by the CG-genome process, which formulates and constructs ZNA objects. In embodiments, ZNA allows VDAXs in the same ecosystem to directly control genome-enabled differentiation processes that do not involve other VDAX components. For example, in embodiments, an EIC VDAX (e.g., cores and memory) can employ ZNA-specific genome processes and other related computational methods to establish differentiation from other specific EIC VDAXs. In embodiments, the EIC module 420 may define a CG-process for detecting genome-based engagement differentiation, which may be computed according to a wide range of information-theoretically accelerated computational complexity functions. In embodiments, the information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0122] In embodiments, the link module 430 defines a set of CG-processes and associated computational methods that enable two VDAXs (e.g., a first VDAX and a second VDAX) to securely exchange information necessary to enable biphasic engagement. In some embodiments, link exchange exhibits the same level of entropy as biphasic engagement. In some embodiments, a link module 430 instance may be configured to verify engagement eligibility and link exchange correlation with another VDAX. In embodiments, engagement eligibility and link exchange correlation enable a pair of VDAXs to successfully exchange links (e.g., spawn links and host links). In embodiments, the link module 430 may be configured to verify engagement eligibility with another VDAX based on its genome engagement object (e.g., CNA or PNA). For example, the link module 430 may verify engagement correlation using its corresponding CNA object and / or verify eligibility synchrony using its corresponding PNA object. In embodiments, a link module of a VDAX (e.g., a first VDAX) may be configured to verify a link exchange-correlation with another VDAX based on the first VDAX's genomic correlation object. In some embodiments, the link module 430 instance spawns a link to another VDAX (e.g., a second VDAX) based on the first VDAX's genomic correlation object (e.g., an LNA object) and information for the other VDAX to engage with the VDAX. In embodiments, the link module 430 instance of a VDAX (e.g., a first VDAX) may host the link, in part, by using the first VDAX's genomic correlation object to decode information for engaging with the other VDAX from links provided by or on behalf of the other VDAX.It should be noted that different configurations of the link module 430 can utilize various CG-genomic processes and associated computational methods to perform secure link exchanges across a wide range of interoperable digital communication media, digital networks, and / or digital devices. It should be noted that link exchanges between VDAXs may be performed asynchronously, in that the order of the exchanges does not affect the security of the protocol. Furthermore, in embodiments, a link exchange may involve one VDAX providing a link to another VDAX (e.g., symmetric), or both VDAXs providing links to their respective other VDAXs (e.g., quasi-symmetric). In embodiments, the link module 430 may define a CG-process for facilitating the exchange of genome-based information, which may be computed according to a wide range of information-theoretically driven computationally complex functions. In embodiments, the information-theoretically driven functions may be cryptographically based, non-cryptographic, or hybrid computationally complex functions.

[0123] As described above, the link may include information that enables biphasic engagement. In embodiments, the information included in the link may include genomic regulatory instructions (GRIs). In some embodiments, the GRIs may define instructions and / or data used to modify a genomic differentiation object (e.g., an XNA or ZNA) in a deterministic manner; if a first VDAX provides a link to a second VDAX and the second VDAX successfully decodes the GRI included in the link, both the first and second VDAXs can use the GRI to modify their respective genomic differentiation objects, resulting in highly correlated copies of the modified genomic differentiation objects (e.g., modified XNAs or modified ZNAs). As used herein, "highly correlated" when used in connection with genomic objects may refer to the same and / or other sufficiently correlated genomic objects, whereby two or more genomic objects are said to be "sufficiently correlated" if the degree of correlation between them allows the intended CG operation or process to be successfully performed. In embodiments, the GRIs may include additional information, such as instructions and / or data used by the VDAXs during sequence mapping. As described in more detail, such deterministic correction allows two cohorts to be distinguished from all other cohorts, enabling the generation of secure VBLS. In embodiments, the link module 430 may generate a GRI for each link such that a unique GRI is generated for any respective engagement. In some embodiments, the link module 430 may encode the GRI using the link-specific engagement coefficient to obtain an encoded GRI. The link module 430 may generate a genomic engagement cargo (GEC) that includes the encoded GRI and additional information used by the link hosting VDAX to decode the GRI from the GEC based on the information and genomic data of the link hosting VDAX.In an embodiment, the link module 430 is further configured to decode the link (which is part of the "link hosting"), whereby the link module 430 obtains a genome coefficient based on information contained in the GEC and its genome dataset, and decodes the encoded GRI using the genome coefficient to obtain a GRI. The decoded GRI can then be used by the link hosting VDAX in generating a VBLS for the link spawn VDAX that provided the link.

[0124] In embodiments, the link module 430 may be further configured to update links. Link updating may refer to a process by which genomic regulatory instructions (GRIs) provided from a first VDAX to a second VDAX for a particular engagement between a pair of VDAXs are modified. Links may be updated for any number of reasons, including concerns that the link has been compromised, and / or according to routine security protocols (e.g., links are updated daily, weekly, or monthly, or in response to a cohort request to update the link). In some embodiments, the link module 430 may update links by generating link update information, whereby the link update information is provided from the VDAX that created the link to the VDAX hosting the link. In embodiments, the link update information may include a new GRI that replaces the current GRI. In other embodiments, the link update information may include data used to modify the current GRI. For example, link updates may be values ​​that the hosting VDAX uses to transform the GRI, such as applying the values ​​to the current GRI using one or more computationally complex functions (e.g., cryptographically based, non-cryptographic, or hybrid functions) to obtain an updated GRI. In some embodiments, link updates differ from link exchanges in that link updates may be encoded in VBLS, as opposed to link exchanges, which may involve more computationally intensive operations. Thus, link exchanges may be performed as a one-time operation, while link updates may be performed any number of times and / or for any suitable reason.

[0125] In embodiments, a link may be a static link or a dynamic link. In embodiments, a dynamic link may refer to a link that includes additional information that further differentiates a pair of cohorts. In some embodiments, a dynamic link may include executable code (or a reference to executable code) used to modify one or more of the functions performed by a pair of VDAXs, but only with respect to their engagement. For example, a dynamic link may include executable code that modifies XNA / ZNA modification functions, sequence mapping functions, and / or binary conversion functions with respect to the respective engagement. In this manner, when a pair of VDAXs exchange dynamic links, the pair of cohorts can execute executable code instead of or in addition to default code when performing a particular function (e.g., XNA / ZNA differentiation, sequence mapping, and / or binary conversion). In embodiments, a static link may refer to a link used in an engagement where the CG-ESP configuration is not modified for the particular engagement.

[0126] In embodiments, the static link module 432 allows two VDAXs (e.g., a first VDAX and a second VDAX) to securely exchange information (e.g., a spawn link and a host link) necessary to enable a unique, symmetric engagement, and this exchange defines a CG process that exhibits the same level of entropy. In embodiments, the rules and processes governing static linking are defined by a top-class VDAX in the ecosystem (e.g., an ecosystem VDAX), whereby the rules may apply to all link VDAXs in the ecosystem. In embodiments, a static link module 432 instance may execute CG processes associated with a CNA used for eligibility correlation and / or a PNA used for eligibility synchronization. In some embodiments, a static link module 432 instance executes CG processes associated with an LNA used for link exchange correlation. In embodiments, a CG platform instance may be configured to execute processes to facilitate secure link exchange across a wide range of interoperable digital communication media, digital networks, and / or digital devices. In embodiments, a VDAX may execute link exchanges asynchronously, in that the order of the exchanges does not affect the security of the protocol. Further, in embodiments, a link exchange may involve one VDAX providing a link to another VDAX (e.g., symmetric), or both VDAXs providing links to their respective other VDAXs (e.g., bi-symmetric). In some embodiments, the link module 432 instance processes genome-based engagement differentiation, which may be computed according to a wide range of information-theoretically facilitated computational complexity functions. In embodiments, the information-theoretically facilitated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0127] In embodiments, the Dynamics Link module 434 defines a CG-process by which two VDAXs (e.g., a first VDAX and a second VDAX) securely exchange the information (e.g., spawn link and host link) necessary to enable a unique biphasic engagement, thereby ensuring that the exchange exhibits the same level of entropy. In embodiments, the rules and processes governing the Dynamics Link are defined by a top-class VDAX in the ecosystem (e.g., an ecosystem VDAX), including the authority to establish additional genomically compatible link exchange instructions and processes.

[0128] In embodiments, the dynamic link module 434 may generate dynamic links that include executable instruction sets (e.g., binary code, scripts, etc.) that modify various CG-processes as permitted by the highest-level VDAX in the CG-enabled ecosystem. In these embodiments, the executable instruction sets in the dynamic link can override the functionality of specific modules (e.g., XNA modules, sequence mapping modules, and / or binary translation modules) for a particular engagement. In this manner, pairs of VDAXs that exchange dynamic links can modify the CG-processes that execute for that particular engagement, which may provide an additional layer of security. In some embodiments, the dynamic link module 434 may include an interpreter or just-in-time compiler that processes the instruction sets included in the dynamic link such that the processed instruction sets are executed for a particular engagement and override one or more CG-processes executed during that engagement. In some embodiments, a first dynamic link module 434 instance may generate a dynamic link that includes executable instruction sets. In these embodiments, the second dynamic link module 434 instance of the second VDAX may decode the dynamic link such that when the second VDAX is generating a VBLS for the first VDAX, both respective dynamic link modules 434 may use the override CG-process(es) for that particular engagement. The second VDAX may use the override CG-process(es) to generate the VBLS, while the first VDAX can use the override CG-process(es) to decode the VBLS.It will be appreciated that data exchange in the reverse direction using a second dynamic link from a second dynamic link module 434 instance to a first dynamic link module 434 instance may operate similarly in that the first VDAX generates a second VBLS using the override CG-process(es) defined in the second dynamic link, and the second VDAX decodes the second VBLS using the override CG-process(es).

[0129] In embodiments, a dynamic linking module 434 instance may establish instructions and associated CG-processes that are not shared by other VDAXs, which may be governed by a wide range of options, circumstances, conditions, and objectives. In embodiments, dynamic linking provides an additional level of security because the CG-processes themselves are modified in a unique way for each unique pair of VDAXs.

[0130] In embodiments, the dynamic link module 434 may perform dynamic link exchanges asynchronously, in that the order of the exchanges does not affect the security of the protocol. Further, in embodiments, the link exchanges performed by the dynamic link module 434 may include one VDAX providing a link to another VDAX (e.g., symmetric), or both VDAXs providing links to their respective other VDAXs (e.g., di-symmetric). In embodiments, the dynamic link module 434 instance processes genome-based engagement differentiation, which may be computed according to a wide range of information-theoretically accelerated computational complexity functions. In embodiments, the information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0131] In embodiments, the sequence mapping module 440 may define a set of CG-processes and computational methods for performing sequence mapping. In some embodiments, sequence mapping may be a non-trivial computation for transforming a unique non-reproduced digital object. In embodiments, a sequence mapping module 440 instance may be configured to map public sequences (e.g., public protocol and / or format-dependent metadata) and / or private sequences (e.g., private and proprietary protocol and / or format-dependent metadata) to a (modified) genome data object. Whether the sequences are public or private, they may be widely heterogeneous (e.g., TCP, UDP, TLS, HTTP, H.265, or other public or private sequences) and may be mapped to the modified genome data to obtain results (e.g., genome engagement factors) that exhibit a particular level of entropy. In embodiments, the sequence mapping module 440 may include a public sequence mapping module 442 and / or a private sequence mapping module 444.

[0132] In embodiments, the public sequence mapping module 442 may define a CG-enabled process and associated methods configured to select specific sequences from public sources (e.g., specific protocol or format-dependent metadata). In some embodiments, a public sequence mapping module 442 instance may process a given public sequence ("PBS1") to derive a specific value ("VI") (e.g., using a hash function or another computationally complex function). In embodiments, the resulting value VI is then processed (e.g., mapped) according to a genome differentiation object (e.g., XNA1) associated with the public sequence to generate a unique vector exhibiting a specific entropy (e.g., genome engagement factor). In embodiments, the value VI may be processed (e.g., mapped) according to an alternative genome differentiation object (e.g., XNA2) to generate a different unique vector exhibiting a specific entropy. In embodiments, the resulting vector may exhibit a level of entropy that significantly exceeds the size of the public sequence used to derive the vector. In embodiments, the public sequence mapping module 442 generates unique vectors that can leverage specific facilities present in unrelated protocols and formats. In embodiments, the public sequence mapping module 442 performs computational genome processing according to information-theoretically accelerated complexity functions to generate unique vectors based on the public sequences and genome differentiation objects (e.g., modified XNA objects). In embodiments, these information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0133] In embodiments, the private sequence mapping module may define a CG-enabled process and associated methods configured to select a specific sequence from a private source (e.g., private and / or proprietary protocol or format-dependent metadata) and derive a unique vector exhibiting a specific entropy. In some embodiments, a private sequence mapping module 444 instance may process a given private sequence ("PVS1") to derive a specific value ("VI"). In embodiments, the resulting value VI may in turn be processed (e.g., "mapped") according to a genome differentiation object (e.g., XNA1) associated with the private sequence module 444 to generate a unique vector exhibiting a specific entropy. In embodiments, the value VI may be processed (e.g., mapped) according to an alternative genome differentiation object (e.g., XNA2) to generate a different unique vector exhibiting a specific entropy. In embodiments, the resulting vector may exhibit a level of entropy significantly exceeding the entropy of the private sequence used to derive the vector. In embodiments, the private sequence mapping module 444 instance generates a unique vector that can leverage specific facilities present in unrelated private protocols and formats. In embodiments, the private sequence mapping module 444 instance performs a computational genomic process according to a set of information-theoretically facilitated computational complexity functions to generate a unique vector based on the private sequence and genome differentiation object (e.g., modified XNA object). As used herein, the term "set of information-theoretically facilitated computational complexity functions" may refer to any combination of one or more information-theoretically facilitated computational complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptography-based functions, cryptography-free functions, or hybrid computational complexity functions that include at least one stage utilizing a cryptography-based function and at least one stage utilizing a cryptography-free function.

[0134] In embodiments, the binary conversion module 450 may define a set of CG-processes and associated computational methods configured to generate a virtual binary (e.g., object-to-object) language script (VBLS). In embodiments, the binary conversion module 450 instance transforms digital objects (e.g., packets, sectors, sequences, and / or frames) having a particular format and protocol using various computational methods (e.g., disambiguation methods and / or encryption methods). In embodiments, the binary conversion module 450 instance is configured to encode a digital object based on a value (e.g., a genome engagement factor) determined by a corresponding sequence mapping module 440 to generate an encoded digital object that may be unique, non-recursive, and / or computationally quantum proof. In embodiments, the binary conversion module 450 instance may be further configured to decode the digital object encoded using the value (e.g., a genome engagement factor) determined by the corresponding sequence mapping module 440. In embodiments, the binary conversion module 450 may include a disambiguation module 452 and / or an encryption module 454.

[0135] In embodiments, the disambiguation module 452 may define a CG-process and computational method to perform a binary transformation of a digital object according to a genomically derived genome engagement factor generated by a corresponding sequence mapping module 440 instance, thereby making the resulting encoded digital object only susceptible to brute-force attacks. In embodiments, the disambiguation module 452 instance may transform the digital object based on the genome engagement factor by performing an XOR operation on the genome engagement factor and the digital object to obtain the encoded digital object. In embodiments, the disambiguation module 452 instance may be configured to receive a different genome engagement factor for each digital object, since the disambiguation technique may be susceptible to more efficient attacks if the same genome engagement factor is used to encode two or more digital objects. In embodiments, the disambiguation module 452 instance may be configured to decode the encoded digital object using an inverse disambiguation function and genome engagement factors. Assuming the genome engagement factor matches the genome engagement factor used to encode the digital object, the inverse disambiguation function outputs a decoded digital object given the genome engagement factor and the encoded digital object. In embodiments, the disambiguation module 452 instances perform genome processing according to information-theoretically facilitated complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0136] In embodiments, the encryption module 454 may define a CG-process and computational method to perform a binary transformation of a digital object according to the genome-derived genome engagement factor generated by the corresponding sequence mapping module 440 instance, so that the resulting encoded digital object is only susceptible to brute-force attacks. In embodiments, the encryption module 454 instance may transform the digital object based on the genome engagement factor using any suitable encryption function that receives the genome engagement factor and the digital object as input and outputs an encoded digital object. In embodiments, the encryption function used must have a corresponding inverse encryption function (or decryption function) that can be used to decrypt the encoded digital object. In embodiments, the encryption module 454 instance may be configured to receive different genome coefficients for each digital object, or may use the same transformation for two or more different digital objects.

[0137] In embodiments, the encryption module 454 instance may be configured to decrypt the encoded digital object using an inverse encryption function and a genome engagement factor. Assuming the genome engagement factor matches the genome engagement factor used to encrypt the digital object, the inverse encryption function outputs a decrypted digital object given the genome engagement factor and the encoded digital object. In embodiments, the encryption module 454 instance performs genome processing according to information-theoretically facilitated computational complexity functions. In embodiments, these information-theoretically facilitated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0138] In embodiments, the authentication module 460 may define CG-processes and computational methods configured to authenticate VDAXs with a common genome architecture. As described, a digital ecosystem built by a highest-level VDAX (e.g., an ecosystem VDAX) has a particular distribution of genome data (e.g., CNA, PNA, LNA, XNA, and / or ZNA) and has particular genome eligibility-correlation, eligibility-sync link exchange-correlation, and / or engagement-correlation attributes. In embodiments, the authentication module 460 instance may be configured to enable a corresponding VDAX to verify engagement correlations with any other VDAXs with a common architecture (e.g., related genome data), regardless of their primary genome architecture (e.g., members of different enclaves within the digital ecosystem). In embodiments, the authentication module 460 may include an inter-cohort module 462 that defines CG-processes and associated computational methods that enable a corresponding VDAX to verify engagement correlations with another VDAX in the same CG-enabled digital ecosystem based on their common genome architecture, regardless of which enclave the VDAXs belong to. In embodiments, the authentication module 460 instance is configured to perform secure genome-based engagement correlation of genomic datasets according to information-theoretically facilitated computationally complex functions, which may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0139] As described, the conformance of the root DNA construct and supporting genomic processes (e.g., link generation, engagement correlation, VBLS generation, etc.) is directly managed and controlled by the specific configuration of the CG-module. In embodiments, each CG-ESP may include a Master Integrity Controller 470 CG-process and associated computational methods that manage module conformance on behalf of VDAX. In embodiments, Master Integrity Controller 470 may include a CG-process and associated computational methods that ensure the veracity of operational performance and configuration management for VDAX across the digital ecosystem. In embodiments, Master Integrity Controller 470 may include a Genomic Process Controller 472, an Authorization Module 474, and an Engagement Instance Module 476.

[0140] In embodiments, the engagement of a VDAX, its genome modules, and other such functional modules may be controlled by a respective master integrity controller 470 instance in each CG-ESP instance (e.g., which may be executed by the corresponding VDAX). In embodiments, the master integrity controller 470 instance utilizes computationally complex functions to engage with specific modules (e.g., lto N). In some of these embodiments, the master integrity controller 470 instance may have a respective genome data set (e.g., CNA, PNA, LNA, and / or XNA) as well as the modules and may use computationally complex functions to engage with and manage specific modules. In some embodiments, the genome process controller 472 may verify the integrity of the modules and authenticate the modules using their genome data and computationally complex functions. In these embodiments, the genome process controller 472 instance is not configured to determine the processes and functions executed by each VDAX. To protect the computationally complex genome processes performed by each VDAX, genome process controller 472 may control operational processes and functions associated with the correct application of module processes and functions, and may render specific operational processes and functions under the control of specific modules for the correct application of specific module processes and functions. In other words, in embodiments, genome process controller 472 may verify the source of a CG-ESP module instance and / or verify or deny the integrity of a CG-ESP instance, as well as any processes and operations performed in support of the module instance (e.g., processes connecting modules to various CG-based functions).

[0141] In embodiments, a VDAX utilizing the same computationally complex genomic functions as the modules and master integrity controller 470 can validate or disqualify a particular CG-ESP configuration. For example, in embodiments, a VDAX (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, and / or a subordinate VDAX) utilizing the same computationally complex genomic functions as the modules and master integrity controller 470 can validate or disqualify a particular CG-ESP configuration. In embodiments, a VDAX utilizing the same computationally complex genomic functions as the modules and master integrity controller 470 can validate, disqualify, or modify a particular CG-ESP configuration. In embodiments, the master integrity controller 470 may include a genomic process controller 472, an authentication module 474, and an engagement instance module 476. In embodiments, the genomic process controller module 472 instance performs secure genomic-based validation, disqualification, and modification of VDAX modules and particular VDAX configurations, which may be computed according to a wide range of information-theoretically driven computationally complex functions. In embodiments, these information-theoretically accelerated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

[0142] In embodiments, VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) offer significant adoption, deployment, and operational flexibility in that configuration control can be influenced horizontally and / or hierarchically. In embodiments, this flexibility stems from the same inherent computationally complex genomic functions (e.g., correlation and differentiation) facilitated by the CG-ESP module. In embodiments, VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) may be uniquely configured and enabled (e.g., master integrity controller 470 intercommunication) such that a single ecosystem or enclave VDAX (e.g., descendant) can determine the operational configuration of other VDAXs. In embodiments, descendants (e.g., ecosystem VDAXs or enclave VDAXs) can directly confirm or disqualify the operational status of other VDAXs based on the configuration of the other VDAXs. In embodiments, an ancestor (e.g., an ecosystem VDAX or an enclave VDAX) may possess unique genomic characteristics that are configured and enabled so that authorization module updates of the VDAX can be performed in coordination with other authorization CG-ESP modules. In embodiments, the master authorization module 474 instance performs secure genomic-based validation, disqualification, and modification of VDAX modules and specific VDAX configurations, which may be computed according to a wide range of information-theoretically accelerated computational complexity functions. In embodiments, these information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0143] In embodiments, engagement between two or more VDAXs (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and / or dependent VDAXs) using computationally complex genomic functions enabled by EG-CSP constitutes a single security instance. In some embodiments, these security instances may be aggregated according to hierarchical genomic relationships exhibited by a particular digital ecosystem community. In embodiments, security instances aggregated at a lower level may be passed to the next or any other higher aggregation point (e.g., from cohort VDAX to enclave VDAX) and so on (e.g., from cohort VDAX and enclave VDAX to ecosystem VDAX). In embodiments, communication between VDAX modules (e.g., security instance reporting) may be based on the same or different computationally complex genomic functions by which their primary security instances are managed. In embodiments, the master engagement instance module 476 instance enables a VDAX (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, and / or a subordinate VDAX) to track security instances according to a set of engagement tracking policies. In some embodiments, these policies may specify how security instances are defined. In embodiments, these definitions may impose specific computationally complex security functions. In embodiments, the master engagement instance module 476 instance enables a VDAX (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, and / or a subordinate VDAX) to calculate the number of security instances to be created according to engagement accounting policies. In embodiments, these policies specify how security instances are accumulated. In embodiments, such accumulation may impose specific computationally complex security functions.In embodiments, the master engagement instance module 476 instance enables VDAXs with a common structure (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to report security instances to other VDAXs according to a set of engagement reporting policies. In embodiments, these policies dictate how security instances are reported, how frequently, and to whom. In embodiments, such reporting imposes specific computationally complex security functions. In some embodiments, a VDAX (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) may be uniquely configured and enabled such that a single VDAX (e.g., ecosystem VDAX) may define digital ecosystem (e.g., community) engagement tracking policies, engagement accounting policies, and / or engagement reporting policies. In some embodiments, a single engagement instance module 476 instance may execute multiple tracking policies, accounting policies, and / or reporting policies using specific computationally complex genomic functions.

[0144] In embodiments, the master engagement instance module enables VDAXs with a common structure (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or dependent VDAXs) to aggregate security instances from other VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or dependent VDAXs). The master engagement instance module 474 provides ecosystem VDAXs with the ability to aggregate security instances from other VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or dependent VDAXs) enabled by specific computationally complex genomic functions in accordance with engagement reporting policies. In embodiments, the master engagement instance module 474 performs secure genome-based tracking, accounting, reporting, and aggregation of VDAX genome-specific security instances, which may be computed according to a wide range of information-theoretically accelerated cryptographic computational complexity functions. In embodiments, these information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0145] It is understood that Figure 4 is provided for illustrative purposes. Additional or alternative modules may be used to configure the CG-ESP without departing from the scope of the present disclosure. As described, different CG-ESPs may be configured to perform different CG-operations on different configurations of genomic datasets. Examples of genomic datasets and different CG-operations performed on genomic data are described in more detail below.

[0146] FIG. 5 illustrates an exemplary implementation of a genomic dataset 300 (also referred to as a "digital DNA set," "DNA set," or "DNA"). As discussed, in embodiments, a CG-ESP (e.g., CG-ESP 400) is configured using a set of CG-processes and associated computational methods that operate on a particular genomic dataset. FIG. 3 illustrates examples of different types of genomic data that may be implemented for different CG-ESPs. It is understood that other types of genomic data may be developed later.

[0147] In embodiments, a DNA set 300 used in connection with CG-ESP may include one or more different types of digitally generated mathematical objects (instances of which may be generically referred to as "genomic data" or "DNA objects") that exhibit configurable entropy. In some embodiments, the digitally generated mathematical objects of a DNA set may include any suitable combination of genome eligibility objects 310, genome correlation objects 320, and / or genome differentiation objects 330. As described, different implementations of each CG-ESP may utilize and support different combinations, types, and sizes of genome data objects depending on the goals of each community owner and / or the type of ecosystem each platform instance supports. Examples of different goals may include performance and efficiency goals, security goals, resource allocation goals (e.g., memory, storage, processing power, network bandwidth, etc.), economic goals, etc. Furthermore, certain ecosystems have different constraints or advantages. For example, certain controlled ecosystems (e.g., some executable ecosystems) may only require certain cohorts (e.g., dependent cohorts of applications, sensors, device drivers, processors, memory devices, etc.) to establish a very limited number of relationships (e.g., via links). In these scenarios, the links for each relationship in the ecosystem may be generated at the time the ecosystem is created, such that each VDAX has access to any and all required links. In such scenarios, a DNA set may not derive any additional benefit from having a particular type of DNA object, and a DNA set for such an ecosystem may be configured without a genome eligibility object 310 or a genome correlation object 320, but may include a genome differentiation object 330.In another exemplary scenario, two or more implementations of engagement eligibility determination, link exchange, and / or differentiation / VBLS generation may be performed using a single DNA object (e.g., via a unique intersection of each DNA object for each pair of VDAXs used for engagement eligibility verification, link exchange, and VBLS determination). In this example, the community owner may be willing to sacrifice additional security measures in order to reduce the storage requirements associated with storing different types of genome data objects. In other scenarios, the community owner may control the amount of entropy exhibited in each type of DNA object in the DNA set based on the type of data structure and / or the size of the data structure selected. For example, a genome differentiation object 330 implemented as a 512 x 512-bit binary vector or bit matrix may provide quantum-proof levels of security.

[0148] In embodiments of the present disclosure, a genomic eligibility object 310 may refer to a digitally generated mathematical object that allows a pair of cohorts to confirm engagement eligibility, which may be performed as part of a "trustless" authentication process between two cohorts. In embodiments, an ancestor VDAX (e.g., an ecosystem VDAX or an enclave VDAX) may derive a descendant genomic eligibility object 310 for a descendant VDAX that will join their respective digital ecosystem based on its genomic eligibility object ("ancestral genomic eligibility object"). In these embodiments, each descendant VDAX may receive a unique but correlated derivation of the ancestral genomic eligibility object. Furthermore, in some implementations, all genomic eligibility objects in an ecosystem may be derived from an ancestral genomic eligibility object, allowing any member of the ecosystem to confirm some relationship with other ecosystem members based on correlated genomic eligibility objects (e.g., intersections or shared portions of ancestral genomic eligibility objects). Once assigned a genomic eligibility object for a particular community, the descendant VDAX may receive that genomic eligibility object. In some embodiments, a descendant VDAX may receive its genome eligibility object in its genome dataset via a one-time trusted event (e.g., upon entry into a particular enclave, upon device manufacture, configuration, or sale, etc.). After receiving each genome correlation object 310, the VDAX may use each genome eligibility object 310 to independently confirm engagement eligibility with one other VDAX in its enclave and / or ecosystem. In embodiments, the genome correlation object 310 for a particular CG-enabled digital ecosystem may be selected from a CNA object 312, a PNA object 314, and / or other suitable mathematical constructs that allow two community members to confirm engagement eligibility and / or engagement completeness.

[0149] In embodiments, a CNA may refer to a genomic mathematical construct that allows a VDAX to uniquely determine that another VDAX is part of the same ecosystem community. In embodiments, this ecosystem correlation may be computationally quantum-proof. In embodiments, the ecosystem correlation performed by a VDAX may be based on a common, computationally complex genomic function and may be performed without any form of consultation with a central authority (e.g., a trusted third party). These correlation attributes allow two VDAXs in the same ecosystem, activated years apart, to verify the state of their ecosystems without prior knowledge of the other and without any consultation with a trusted third party.

[0150] In embodiments, the CNA object 312 may be implemented as a binary vector, a binary matrix, or the like. In embodiments, the CNA object 312 is configured to exhibit a particular entropy. In some embodiments, the entropy of the ecosystem's CNAs is controllable, whereby the entropy may be configured, for example, by a community owner. In some implementations, the configurable entropy level of the CNA object 312 may be a substantially quantum-proof level of entropy. For example, a substantially quantum-proof CNA object may be configured to exhibit a level of entropy of 256 bits or greater. For example, in some embodiments, such a level of entropy may be achieved by a CNA object implemented as a 512 x 512 bit binary vector or matrix. It is understood that quantum-proof CNA objects 312 may exhibit less entropy in some example implementations. It is understood that CNA objects 312 exhibiting less entropy may be used (e.g., as determined by a community owner or any other party configuring the security platform). For example, a community owner may wish to comply with jurisdictional regulations and therefore may use CNA objects (or other genomic datasets) that exhibit lower levels of entropy, which requires less storage and processing demands, at the expense of overall security. In embodiments, CNA 312 may be configured to use a series of genomic processes and associated computational methods to establish specific relationships between individual ecosystem members and verify eligibility for engagement.

[0151] In embodiments, CNA generation for genomic eligibility-correlation applications results in a large set of random data that can be organized as specific binary vectors. In some embodiments, CNA generation for genomic eligibility-correlation applications may be enabled by a high-quality random process with controllable entropy. In embodiments, CNA generation for genomic eligibility-correlation applications may be enabled based on specific mathematical principles with controllable entropy. In embodiments, CNAs may be generated according to a wide range of information-theoretically-driven complex functions. In embodiments, these information-theoretically-driven functions may be cryptographically-based, cryptographically-free, or hybrid computationally complex functions. Exemplary techniques for generating CNA objects, modifying CNA objects, and verifying eligibility for engagement are discussed in more detail throughout this disclosure.

[0152] In embodiments, a PNA may refer to a digital entity that allows a VDAX to uniquely determine that another VDAX is part of the same ecosystem community. In embodiments, this ecosystem correlation may be computationally quantum-proof. In embodiments, the ecosystem correlation performed by a VDAX may be based on a common, computationally complex genomic function and performed without any form of consultation with a central authority (e.g., a trusted third party). These correlation attributes allow two VDAXs in the same ecosystem, activated years apart, to verify the state of their ecosystems without prior knowledge of the other and without any consultation with a trusted third party.

[0153] In embodiments, PNA objects 314 may be implemented as a set of binary primitive polynomials, or the like. In embodiments, PNA objects 314 are configured to exhibit a particular entropy. In some embodiments, the entropy of PNA objects 314 in an ecosystem is controllable, whereby the entropy may be configured, for example, by a community owner. In some implementations, the configurable level of entropy of PNA objects 314 may be a substantially quantum-proof level of entropy. For example, substantially quantum-proof PNA objects 314 may be configured to exhibit a level of entropy of 256 bits or more. For example, in some embodiments, such a level of entropy may be achieved by a PNA object implemented as two different sets (e.g., a first vector representing a 2048 x 2048 bit binary matrix and a second vector representing a set of 216 randomly chosen binary primitive polynomials of degree 256). It is understood that quantum-proof PNA objects 314 may exhibit less entropy in some example implementations. It is understood that PNA objects 314 having less entropy may be used (e.g., as determined by the community owner or any other party comprising the security platform). For example, a community owner may wish to comply with jurisdictional regulations and therefore use PNA objects (or other genomic datasets) exhibiting lower levels of entropy, requiring less storage and processing demands, at the cost of overall security. In embodiments, a PNA may be configured to use a series of genomic processes and associated computational methods to establish specific relationships between individual ecosystem members and verify eligibility for engagement.

[0154] In embodiments, PNA generation for genome eligibility-correlation applications results in a large set of random data that can be organized as a specific binary vector. In some embodiments, PNA generation for genome eligibility-correlation applications can be enabled by a high-quality random process with controllable entropy. In embodiments, PNA generation for genome eligibility-eligibility-synchronization applications can be enabled based on a specific mathematical basis with controllable entropy. Exemplary techniques for generating PNA objects, modifying PNA objects, and verifying eligibility for engagement are described in more detail throughout this disclosure. Exemplary techniques for generating PNA objects, modifying PNA objects, and verifying eligibility for engagement are discussed in more detail throughout this disclosure.

[0155] In an embodiment, a genome correlation object 320 may refer to a digitally generated mathematical object that enables VDAXs to establish correlations with one another. In an embodiment, a genome correlation object enables link exchange between VDAXs, whereby a first VDAX generates a link (also referred to as a "link") that is provided to and hosted by a second VDAX, whereby the link may provide instructions that the second VDAX uses to generate a VBLS that can only be decoded by the first cohort (assuming the link is securely held by the second VDAX). In an embodiment, the genome correlation object 310 is used by the CG-ESP to verify the link exchange correlation, thereby enabling two ecosystem components (e.g., an enclave VDAX, a cohort VDAX, etc.) to establish a specific relationship and engage with one another.

[0156] In an exemplary implementation of CG-ESP, the genomic correlation objects 320 of digital ecosystem community members are implemented as LNA objects 322. In embodiments, LNAs form the basis for VDAX to establish correlations with each other. The entropy exhibited by the LNA objects 322 is important in terms of the quality of the correlation. It is a non-recurrent correlation attribute that can be derived from specific computationally complex genomic functions. In some implementations of CG-ESP, LNAs can be generated (e.g., by the ecosystem VDAX) for genomic correlation applications on large sets of random data. In some embodiments, the LNA objects 322 are implemented as binary vectors, bit matrices, or other suitable structures. In embodiments, the LNA objects 322 are configured to exhibit configurable entropy, such that the level of entropy exhibited by the LNA objects can be a factor in the overall degree of correlation. In embodiments, LNA generation may be performed by a high-quality random process with controllable entropy. In some embodiments, LNA generation for genomic correlation applications may be enabled based on specific mathematical principles with controllable entropy. In embodiments, the LNA may be generated according to a wide range of information-theoretically accelerated complex functions, which may be cipher-based, cipher-less, or hybrid computational complexity functions.

[0157] In embodiments, a pair of VDAXs can engage in bi-symmetric link exchange and / or uni-directional link exchange based on their common LNAs (e.g., both VDAXs were assigned their respective LNAs from the same ancestor). In embodiments, a first VDAX may modify its LNA object and encode genomic regulatory instructions ("GRI") based on the modified LNAs such that the second cohort is the only other VDAX capable of decoding the mapped GRI. In embodiments, the GRI may include data (e.g., one or more values) and instructions indicating how the data is used to differentiate the pair of VDAXs for data exchange. In embodiments, the GRI may be used to modify a differentiation object such that the data included in the GRI includes differentiation values ​​(e.g., embodied as binary vectors) that are used as input parameters to an information-theoretically driven, computationally complex function that modifies the genomic differentiation object based on the differentiation values. In some embodiments, the GRI may include sequence modification values ​​used during the sequence mapping process. In these embodiments, the sequence mapping process may be used as input parameters to an information-theoretic-accelerated computational complexity function that modifies the sequence to an intermediate value based on the derivative value, and the intermediate value and modified derivative object may be used as input values ​​to an information-theoretic-accelerated computational complexity function that outputs a genome engagement value corresponding to the original sequence.

[0158] It is understood that encoding genome regulatory instructions based on modified LNAs may involve intermediate operations. For example, in some implementations of CG-ESP, a VDAX may be configured to determine a mapping sequence, map the mapping sequence to a modified LNA using a computationally complex function, and encode a GRI based on the genome engagement factor. In these implementations, a VDAX may provide links to other VDAXs so that if the other VDAXs have highly correlated LNAs, the other VDAXs can successfully decode the encoded GRI. In some implementations of CG-ESP, LNA objects in a VDAX may be highly correlated if they are identical or otherwise sufficiently correlated. In some embodiments, link exchange is a one-time process that is performed only once between a pair of cohorts unless one of the cohorts explicitly updates its respective link to modify the GRI. Outside of such operations, a pair of cohorts can continue to exchange data based on the respective links that each of the cohorts generates, even in some scenarios where the LNA objects of each VDAX are mutated (e.g., persistently modified) by or at the direction of, for example, an ancestral VDAX after a successful link exchange. Examples of genome operations involving LNA objects 322 are discussed in more detail throughout this disclosure, including techniques for generating LNA objects 322, techniques for modifying LNA objects 322, and techniques for performing link exchanges using LNA objects 322.

[0159] In embodiments of the present disclosure, a genome differentiation object 330 may refer to a digitally generated mathematical object that allows pairs of community members (e.g., cohorts) to exchange and decode the VBLS they generate when they successfully exchange links and have sufficiently correlated genome differentiation objects. In some embodiments, a first VDAX generates a second VDAX's VBLS in part by modifying its genome differentiation object 330 in a manner defined in genome regulatory instructions (GRIs) provided to the first VDAX with links from the second VDAX, and decodes VBLS from the second cohort in part by modifying its genome differentiation object 330 according to the GRIs provided to the second cohort. In CG-ESP embodiments, the first VDAX may map a sequence (e.g., a private or public sequence) to a modified XNA object using a computational complexity function (e.g., a cryptography-based, cryptography-free, or hybrid computational complexity function) to obtain a genome engagement factor, which is used to encode the digital object. Examples of genomic identification objects 330 may include, but are not limited to, XNA 332 objects and ZNA 334 objects.

[0160] In an exemplary implementation, the genome difference object 330 of a digital ecosystem community member is an XNA. In some embodiments, XNA is the core competency upon which all genome differences rely. In these embodiments, XNA forms the basis of the two-dimensional symmetric language (e.g., VBLS) that VDAX employs to control unique non-recursive engagement. In some embodiments, the unique non-recursive engagement can be quantum-resistant. In embodiments, the entropy exhibited by XNA can be important from a VBLS security perspective, with higher entropy providing a higher level of security. In embodiments, the recursive difference attribute is derived from a specific computationally complex genome function. In embodiments, XNA generation for genome differentiation applications results in a large set of random data that can be organized as specific binary vectors. In embodiments, XNA generation for genome differentiation applications can be performed by a high-quality random process with controllable entropy. In some embodiments, XNA generation for genome differentiation applications can be enabled with specific mathematical foundations with controllable entropy. In embodiments, XNAs can be generated according to a wide range of information-theoretically driven complex functions. In embodiments, these information-theoretic facilitated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

[0161] In some embodiments, the XNA object 332 may be implemented as a binary vector, matrix, or the like, indicating a configurable entropy. In some embodiments, the entropy indicated by the XNA object 332 determines the security of the VBLS generated by the community members. In some embodiments, the XNAs assigned to each community member (e.g., enclave member) from an ancestor VDAX are identical and / or otherwise sufficiently correlated. In some embodiments, a first VDAX generating a VBLS for a second VDAX modifies its XNA object 332 according to the GRI provided by the second VDAX in the link. The first VDAX may then map a sequence (e.g., a public sequence or a private sequence) determinable by the second VDAX to the modified XNA object 332 to obtain a genome engagement factor. A digital object (e.g., processor instructions, packet payload, disk sector, etc.) may then be encoded using cryptographic-based encryption or disambiguation and the genome engagement factor to obtain an encoded digital object included in the VBLS object. In embodiments, the VBLS object may further include metadata, such as the sequence used to generate the genomic engagement factor. The encoded digital object of the VBLS results may then be provided to a second cohort. In these exemplary implementations, the second cohort receives the VBLS object, modifies its XNA332 according to the GRI included in the link provided by (or on behalf of) the second VDAX to the first VDAX, and maps the sequence to the modified XNA to recreate the genomic engagement factor. The genomic engagement factor may then be used to decode the encoded digital object using the cryptographically-based decryption or disambiguation used to encode the digital object.In these exemplary implementations, the ability of a VDAX to both modify its respective XNA objects 332 using the same GRI and determine the genome engagement factor in a deterministic manner allows a first cohort to securely provide data objects to a second VDAX, potentially varying the genome engagement factor with every instance of data exchange (e.g., every packet, every sector, every shard, every frame, etc.). In this manner, VBLS possesses quantum-proof levels of security. Note that the foregoing description is an example of how XNA or other genome-differentiated objects may be leveraged in a secure data exchange process.

[0162] In some embodiments, revocation of a community member (e.g., a cohort) from a community (e.g., an enclave) can be accomplished by a descendant VDAX selectively mutating the XNA objects of some community members within the community. Note that "mutating" an XNA object can refer to providing a descendant VDAX with instructions to permanently modify that XNA object or providing a new XNA object to the descendant VDAX. In this manner, the mutated XNAs are used for subsequent VBLS coding and encoding for the particular community. For example, in some exemplary implementations, an ecosystem VDAX can mutate the XNAs of only those cohorts that remain in the enclave. In this manner, a cohort that has been revoked from the enclave can still engage with cohorts, but cannot generate VBLS for or decode VBLS from cohorts that have mutated XNA objects. If a community owner (e.g., a network administrator associated with the ecosystem and / or an enclave in the ecosystem) chooses to revive the cohort, the enclave VDAX may also mutate the cohort's XNA so that it is sufficiently correlated with other community members whose XNAs were previously mutated, and then allow the cohort to begin exchanging data with other cohorts within the enclave using previously established links and / or links that it will establish in the future.

[0163] In an exemplary implementation, the genome difference object 330 of a digital ecosystem community member is a ZNA. In some embodiments, the ZNA is the core capability upon which all executable, separate component genome differences rely. In these embodiments, the ZNA forms the basis upon which unique, non-recurring (potentially quantum-proof) executable binaries are controlled. The EIC iterative transformation may be derived from a specific computationally complex genome function. In embodiments, ZNA generation for genome differentiation applications results in a large set of random data that can be organized as a specific binary vector. In embodiments, ZNA generation for genome differentiation applications may be performed by a high-quality random process with controllable entropy. In some embodiments, ZNA generation for genome differentiation applications may be enabled based on a specific mathematical basis with controllable entropy. In embodiments, ZNAs may be generated according to a wide range of information-theoretically driven complex functions. In embodiments, these information-theoretically driven functions may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0164] In some embodiments, the ZNA object 334 may be implemented as a binary vector, matrix, or the like, whereby the ZNA object 334 exhibits configurable entropy. In some embodiments, the ZNA may be structurally similar to an XNA, but used in an executable ecosystem. In embodiments, the ZNA may be used to generate VBLSs exchanged between components of the executable ecosystem. In some embodiments, the entropy exhibited by the ZNA object 334 determines the safety of the VBLSs generated by community members. In embodiments, the ZNAs assigned to each community member (e.g., device component) from an ancestor VDAX are identical and / or otherwise sufficiently correlated. In some embodiments, a first VDAX (e.g., a first EIC) generating a VBLS for a second VDAX (e.g., a second EIC) modifies its ZNA object 334 according to the GRI provided by the second VDAX in the link. The first VDAX can then map a sequence (e.g., a public sequence or a private sequence) determinable by the second VDAX to the modified ZNA object 334 to obtain a genome engagement factor. A digital object (e.g., a processor instruction, a disk sector, etc.) may then be encoded using the complex function and the genome engagement factor to obtain an encoded digital object that is included in the VBLS object. In embodiments, the VBLS object may further include metadata, such as the sequence used to generate the genome engagement factor. The VBLS result encoded digital object may then be provided to the second VDAX. In these exemplary implementations, the second VDAX receives the VBLS object, modifies its ZNA object 334 according to the GRI included in the link provided to the first VDAX on behalf of the second VDAX, and maps the sequence to the modified ZNA object 334 to recreate the genome engagement factor.The genomic engagement factor may then be used to decode the encoded digital object using the inverse of the bidirectional function used to encode the digital object. In these exemplary implementations, the ability of a VDAX to both modify its respective ZNA object 334 using the same GRI and determine the genomic engagement factor in a deterministic manner allows a first cohort to securely provide data objects to a second VDAX, potentially varying the genomic engagement factor for each instance of data exchange (e.g., every packet, every sector, every shard, every frame, etc.). In this manner, VBLS can provide quantum-proof levels of security. Note that the foregoing description is an example of how a ZNA or other genomic identification object can be leveraged in a secure data exchange process.

[0165] As can be appreciated from the present disclosure, core genome competencies (e.g., differentiation and correlations that support CG-ESP processes) are the generation (e.g., DNA generation, which may include LNA generation, XNA generation, ZNA generation, CNA generation, and / or PNA generation), modifications (e.g., DNA modifications, which may include LNA modifications, XNA modifications, ZNA modifications, CNA modifications, and / or PNA modifications), and distribution (e.g., DNA distribution, which may include LNA distribution, XNA distribution, ZNA distribution, CNA distribution, and / or PNA distribution) of specific genomes (e.g., digital DNA, which may include some combination of LNA, XNA, ZNA, CNA, and / or PNA). In embodiments, these application-specific DNA constructs (e.g., some combination of LNA, XNA, CNA, PNA, and / or ZNA) have specific transformations and are important for the controllable visualization of differentiation.

[0166] In embodiments, an ecosystem ancestor (e.g., ecosystem VDAX) may mutate (e.g., persistently modify) the genomic data 300 of some or all of the ecosystem members. In embodiments, mutation of genomic data 300 may refer to persistent modification or updating of genomic data objects. For example, in embodiments, the ecosystem may mutate LNA objects 322, XNA objects 332, CNA objects 312, and / or PNA objects 314 of some or all members of the ecosystem such that VDAX uses the mutated genomic data instead of the previous genomic data. Note that the term “mutation” may be used to refer to modifications to DNA objects 300 that are persistent, as opposed to modifications during link exchange or VBLS generation, which may be transient modifications. Note, however, that modifications and mutations may have similar effects on DNA constructs, and that the term “modification” may be used in connection with persistent modifications when the context so suggests.

[0167] In embodiments, community member LNA objects may be modified (e.g., for link exchange) and mutated (e.g., continuously modified / updated). As described, non-recurring correlation objects (e.g., LNAs) may derive from specific computationally complex genomic functions, and this correlation may involve a digital ecosystem with dimensions N×M, consisting of VDAXs with various enclave relationships N×M. Such digital ecosystem relationships may need to modify their correlation attributes to prevent the establishment of future or additional ecosystem relationships. LNA mutation allows for specific (broad and narrow) redetermination of correlation attributes. In embodiments, the LNA genomic structure can be tailored to a specific digital ecosystem organization, and the structure is modifiable. In some embodiments, the LNA random vector can be modified uniformly or unobtrusively (broad and narrow) based on specific instructions. LNA modification maintains the genomic integrity of the LNA construct and its correlation attributes. In embodiments, VDAXs possessing modified LNAs cannot affect future correlations with VDAXs possessing unmodified LNAs. In embodiments, LNAs may be genomically engineered according to a wide range of information-theoretically driven cryptographic complexity functions, which may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0168] In embodiments, the XNAs of community members may be mutated (e.g., continuously modified / updated). As described, non-recurrent differentiation objects (e.g., XNAs) may be derived from specific computationally complex genome functions, and this differentiation may include a digital ecosystem with dimensions N×M, consisting of VDAXs with various enclave relationships N×M. Such digital ecosystem relationships may require modification of their differentiation attributes (e.g., relationship revocation), which is one of the most challenging problems in security management. XNA mutation allows for specific (broad and narrow) redetermination of differentiation attributes, efficiently solving the relationship revocation challenge. In embodiments, the XNA genome architecture can be tailored to a specific digital ecosystem organization, and the architecture is modifiable. In some embodiments, the XNA random vector can be modified uniformly or unobtrusively (broad and narrow) based on specific instructions. XNA modification maintains the genomic integrity of the XNA construct and its correlation attributes. In embodiments, VDAXs carrying mutated XNAs cannot affect future differentiation with VDAXs carrying non-mutated XNAs. In embodiments, XNAs may be genomically mutated according to a wide range of information-theoretically accelerated cryptographic complexity functions. In embodiments, these information-theoretically accelerated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions.

[0169] In embodiments, a community member's CNA object 312 may be mutated (e.g., continuously modified / updated). As described, a non-recurring eligibility object (e.g., a CNA or PNA) may be derived from a specific computationally complex genomic function, and this modification may involve a digital ecosystem with dimensions N×M consisting of VDAXs with various enclave relationships N×M. Such digital ecosystem relationships may require modification of differentiating attributes (e.g., relationship expiration), which is one of the most challenging problems in security management. Modification of a VDAX ecosystem's eligibility object maintains a common computationally complex genomic function. Such digital ecosystem relationships may require modification of its eligibility object, preventing VDAXs from establishing future or additional ecosystem relationships. Mutation of a CNA or PNA allows for redetermining the eligibility object's specificity (broad and narrow).

[0170] In embodiments, CNA genome construction can be tailored to specific digital ecosystem organizations, and the construction is modifiable. In some embodiments, CNA random vectors can be uniformly or unobtrusively (broadly and narrowly) modified based on specific instructions. CNA modification maintains the genomic integrity of the CNA construct and its fitness-correlation attributes. In embodiments, VDAXs possessing mutated CNAs cannot establish future fitness-correlations with VDAXs of unmutated CNAs. In embodiments, CNAs can be genomically mutated according to a wide range of information-theoretically driven computational complex functions. In embodiments, these information-theoretically driven functions can be cryptographically based, cryptographically free, or hybrid computational complex functions.

[0171] In embodiments, PNA genome construction can be tailored to specific digital ecosystem organizations, and the construction is configurable. In some embodiments, PNA random primitive polynomials can be modified uniformly or unobtrusively (broadly and narrowly) based on specific instructions. PNA modification maintains the genome integrity of the PNA construction and its eligibility synchronization attributes. In embodiments, VDAXs possessing mutated PNAs cannot establish future eligibility synchronization with VDAXs possessing unmutated PNAs. In embodiments, PNAs can be genomically mutated according to a wide range of information-theoretically driven computationally complex functions.

[0172] In embodiments, ecosystem ancestry (e.g., ecosystem VDAX) can assign DNA to community members (e.g., enclaves, cohorts, etc.). In embodiments, each specific DNA construct has a unique genomic relationship. LNAs provide correlation, XNAs provide differentiation, CNAs provide engagement-integrity, and PNAs provide engagement-competence. The overall capabilities facilitated by these structures are substantially derived from the relationships of their genomic mathematical structures, ultimately resulting in the assignment of a specific VDAX. These VDAX relationships may change according to specific modifications of the DNA (e.g., LNAs, XNAs, CNAs, and PNAs).

[0173] In embodiments, an ecosystem ancestor (or appropriate ancestor VDAX) can assign LNAs to community members. In embodiments, the LNA correlation capacity belongs to all digital ecosystems with dimensions N×M, consisting of VDAXs, which may have various enclave and cohort relationships N×M, and such digital ecosystems also have LNA correlation capacity. In embodiments, LNA genome-based constructs are assigned to specific digital ecosystem VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to determine their associated correlation capacity. In embodiments, LNA assignments maintain the genomic integrity of the LNA construct and its correlation attributes. In embodiments, a VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) whose initial LNA assignment has been corrected can no longer influence correlations with VDAXs carrying uncorrected LNAs, but may now be able to influence future correlations with other VDAXs with the same corrected LNA assignment. In embodiments, LNAs may be genomically assigned according to a wide range of information-theoretically driven computationally complex functions, which may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0174] In embodiments, an ecosystem ancestor (or appropriate ancestor VDAX) can assign XNAs to community members. In embodiments, XNA differentiation capabilities belong to all digital ecosystems with dimensions N×M consisting of VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs), which can also have various enclave and cohort relationships N×Ma. In embodiments, constructs based on XNA genomes are assigned to specific digital ecosystem VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to determine their associated differentiation capabilities. In embodiments, XNA assignment maintains the genomic integrity of the XNA construct and its differentiation attributes. In some embodiments, a VDAX whose initial XNA allocation has been modified (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, etc.) can no longer influence differentiation with VDAXs possessing unmodified XNAs, but may now influence differentiation with other VDAXs possessing the same modified XNA allocation. In embodiments, XNAs may be genomically assigned according to a wide range of information-theoretically driven, computationally complex functions. In embodiments, these information-theoretically driven functions may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0175] In embodiments, an ecosystem ancestor (or appropriate ancestor VDAX) can assign CNAs to community members. In embodiments, CNA engagement-integrity capabilities belong to all digital ecosystems with dimensions N×M consisting of VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs), which may also have various enclave and cohort relationships N×Ma. In embodiments, CNA genome-based constructs are assigned to specific digital ecosystem VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs). In embodiments, these CNA genome-based constructs determine their associated engagement-integrity capabilities within the ecosystem. In some embodiments, CNA genome-based constructs assigned to specific digital ecosystem VDAXs may also be unique.

[0176] In embodiments, CNA assignments maintain the genomic integrity of the CNA construct and its engagement integrity attributes. In some embodiments, a VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) whose initial CNA assignment has been modified may no longer be able to influence engagement integrity with VDAXs carrying unmodified CNAs, but may now be able to influence engagement integrity with other VDAXs with the same modified CNA assignment. In embodiments, CNAs may be genomically assigned according to a wide range of information-theoretically driven computational complexity functions. In embodiments, these information-theoretically driven functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0177] In embodiments, an ecosystem ancestor (or appropriate ancestor VDAX) can assign PNAs to community members. In embodiments, PNA engagement-eligible capabilities belong to all digital ecosystems with dimensions N×M, composed of VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs), which may also have various enclave and cohort relationships N×Ma. In embodiments, PNA genome-based constructs are assigned to specific digital ecosystem VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to determine their associated engagement-eligible capabilities. In embodiments, PNA genome-based constructs assigned to specific digital ecosystem VDAXs may also be unique.

[0178] In embodiments, PNA allocation maintains the genomic integrity of the PNA construct and its engagement-eligibility attributes. A VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) whose initial PNA assignment has been modified may no longer be able to influence engagement eligibility with VDAXs possessing unmodified PNAs, but may now be able to influence engagement eligibility with other VDAXs with the same modified PNA assignment. In embodiments, PNAs may be genomically assigned to VDAXs according to a wide range of information-theoretically driven, computationally complex functions. In embodiments, these information-theoretically driven functions may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0179] As previously mentioned, pairs of well-correlated VDAXs can be engaged using links. In embodiments, the primary purpose of links is to enable pairs of VDAXs to exchange information necessary to perform higher-level, computationally complex genomic functions. In embodiments, the information exchanged in links is referred to as genome-engagement-cargo (GEC). In embodiments, link processing may include link generation, link hosting, and link update. Link generation may refer to the creation and transport of a link by a spawning VDAX. Link hosting may refer to the retrieval and integration of information contained in a link by a receiving VDAX. Link update may refer to the CG process that changes the genome infrastructure used by a VDAX to engage with other VDAXs. The process of link update may also be referred to as "link correction." In embodiments, the linking processes (spawning, hosting, updating) depend on specific information-theoretic configurations. For example, in embodiments, LNAm may be used as the basis for genome correlation, CNAm may be used as the basis for genome engagement-completeness, and PNA may be used as the basis for genome engagement-eligibility. These DNA constructs (e.g., LNAs, CNAs, and PNAs) are application-specific genome constructs that enable specific genome transformation functions that facilitate the linking process. In embodiments, the linking process may be defined in the link module 430 of the CG-ESP, such that some or all of a CG-ESP instance may be configured with link process module 430 instances that perform these functions. For example, any VDAX that requires the role of creating, hosting, and / or updating links may define processes for static linking and / or dynamic linking and may be configured with such a link process module 430 instance.

[0180] In embodiments, a pair of VDAXs (e.g., a first VDAX and a second VDAX) belonging to the same CG-enabled digital ecosystem can create and host a link without prior arrangement. In these embodiments, a VDAX (e.g., a first VDAX) intends to create a genome, and a VDAX (e.g., a second VDAX) intends to create a genome. The link for receipt and use of a genome engagement cargo (GEC) by another VDAX (e.g., the second VDAX) utilizes its CNA to establish engagement-consistency with the other VDAX (e.g., the second VDAX) from which the link was created. In some embodiments, a VDAX (e.g., a first VDAX) generates a genomic link for receipt and use of the contained GEC by another VDAX (e.g., a second VDAX), whereby a pair of VDAXs (e.g., a first and second VDAX) may have multiple genomic links that utilize the same CNA to establish engagement integrity.

[0181] In embodiments, a VDAX (e.g., a first VDAX) seeking to create a genomic link for receipt and use of a GEC by another VDAX (e.g., a second VDAX) can utilize its PNA to establish engagement eligibility with the other VDAX from which the link was created. In some embodiments, a VDAX (e.g., a first VDAX) can create a genomic link for receipt and use of the contained GEC by another VDAX (e.g., a second VDAX), thereby allowing a pair of VDAXs (e.g., a first and second VDAX) to have multiple genomic links utilizing the same PNA to establish engagement eligibility. Note that in some embodiments, the GECs included in the link may include additional link activation requirements.

[0182] In some embodiments, a generating VDAX generating a link for transmission and use (e.g., "link hosting") by another VDAX (e.g., a second VDAX) can utilize its LNA to establish genomic correlation with the other VDAX from which the link was generated. As described, LNA-based genomic processes may enable an entire digital ecosystem (community) to achieve correlation between VDAXs based on a single genomic structure (e.g., an LNA). In embodiments, LNA-based genomic processes enable VDAXs to modify their respective LNA constructs by using specific computationally complex functions, whereby these LNA-based genomic processes utilize a substructure of genomic information (e.g., an LNA-based genomic substructure). In embodiments, the LNA-based genomic substructure can be utilized to compute unique transformation information from the link that yields the VDAX, which can only be reproduced by the link hosting the VDAX, with the same level of entropy as the underlying computationally complex genomic function. In embodiments, unique genomic engagement factors are utilized to prepare GECs for digital transport from the spawning VDAX to the hosting VDAX. In some of these embodiments, the link hosting VDAX may use a unique genome engagement factor to decode the encoded GRI contained in the GEC. In some embodiments, the unique genome engagement factor may be rendered as multiple sub-configurations for application in multiple digital transmission channels.

[0183] In some scenarios, ecosystem correlation is unavailable. In some embodiments, VDAX authentication may be required for link spawning and hosting when ecosystem correlation is unavailable. In these embodiments, VDAX authentication may be achieved through the use of alternative genome substructures to facilitate free-form correlation (FFC). For example, a scenario may arise in which a pair of VDAXs reside in a unique genome digital ecosystem (which may be referred to as a "republic"). In some embodiments, these unrelated VDAXs may form a unique genome digital ecosystem (which may be referred to as a "federation") for specific operations and uses. In these embodiments, VDAXs may spawn links not only within their respective republics, but also as members of a federation.

[0184] In embodiments, the link-spawn genome processes may be implemented according to a wide range of information-theoretically accelerated computational complexity functions that accelerate the execution of genome functions and processes. In embodiments, these information-theoretically accelerated functions may be cryptographically based, cryptographically free, or hybrid computational complexity functions.

[0185] As described above, genome link hosting (or "link hosting") may involve the acquisition and integration of link information by a VDAX (e.g., a second VDAX) so that the link contains a specific genome engagement cargo (GEC) from another VDAX (e.g., a first VDAX). In embodiments, link hosting may be performed according to a specific computationally complex genome function. In embodiments, the hosting VDAX (e.g., a second VDAX) receives a unique transformation information substructure via one or more digital transmission channels. In embodiments, a hosting VDAX (e.g., a second VDAX) that wishes to use (host) the genome engagement cargo (GEC) transported by a link spawned by a spawn VDAX (e.g., a first VDAX) can utilize its CNA to establish engagement integrity with the spawn VDAX. In an embodiment, if a hosting VDAX (e.g., a second VDAX) wishes to use (host) a genome-engagement cargo (GEC) transported by a link generated by a spawning VDAX (e.g., a first VDAX), it can utilize its PNA to establish engagement-eligibility with the spawning VDAX.

[0186] In embodiments, a hosting VDAX can utilize LNAs that enable correlation of its digital ecosystem by modifying its LNAs with a specific computationally complex function that utilizes a unique transformation information substructure. In embodiments, the LNA-based genome substructure is used to calculate a unique genome engagement factor by the link-hosting VDAX with the same level of entropy as the underlying computationally complex genome function. In embodiments, the hosting VDAX (e.g., a second VDAX) uses the unique genome engagement factor to extract a GEC from the link provided by the spawning VDAX (e.g., a first VDAX). In embodiments, the hosting VDAX (e.g., a second VDAX) may be required to complete additional link activation requirements imposed by the spawning VDAX, whereby the additional link activation requirements are provided in the GEC.

[0187] As previously mentioned, scenarios may arise where a pair of VDAXs are in a unique genomic digital ecosystem (which may be referred to as a "republic"). In some embodiments, these unrelated VDAXs may form a unique genomic digital ecosystem (which may be referred to as a "federation") for certain operations and uses as described above.

[0188] In embodiments, the link-hosting genomics process may be implemented according to a wide range of information-theoretically driven computational complexity functions. In embodiments, these information-theoretically driven functions may be cryptography-based, cryptography-free, or hybrid computational complexity functions. In embodiments, these functions may be necessary to perform genomics operations.

[0189] In embodiments, a VDAX may update links hosted by other VDAXs. For example, to increase a level of security, a VDAX may update links hosted by other VDAXs to reduce the likelihood that a malicious party will determine or otherwise obtain link information (e.g., GRI). In these embodiments, a pair of VDAXs (e.g., a first VDAX and a second VDAX) that have previously completed the "link generation" and "link hosting" protocols may update one or both links. In this manner, a VDAX (e.g., a first VDAX) may change the genome infrastructure used to engage with another VDAX (e.g., a second VDAX), and / or vice versa. In some embodiments, a new genome link created by a VDAX (e.g., a first VDAX) and sent to another VDAX (e.g., a second VDAX) for hosting may be used to replace one or more existing links hosted by the other VDAX with the newly created link, thereby updating the link. In embodiments, a genomic link produced by a VDAX and submitted for hosting to another VDAX may be used to modify some or all of the GRI data of an existing hosted link.

[0190] As mentioned above, scenarios may arise where a pair of VDAXs are in a unique genomic digital ecosystem (which may be referred to as a "republic"). In some embodiments, these unrelated VDAXs may form a unique genomic digital ecosystem (or "federation") for specific operations and uses. In some of these embodiments, a federation of VDAXs may also update their links for specific operations and uses.

[0191] In embodiments, the link update genome process may be performed according to a wide range of information-theoretically accelerated cryptographic complexity functions. In embodiments, these information-theoretically accelerated functions may be cryptographic-based, cryptographic-free, or hybrid computational complexity functions necessary to perform genome functions and processes.

[0192] As described throughout this disclosure, sequence mapping and binary conversion are CG operations that may be performed to form a VBLS. In embodiments, sequence mapping may be performed on public and / or private sequences. In embodiments, a sequence may refer to a sequence of data (e.g., a sequence of bits). In embodiments, a public sequence may refer to public protocol- and format-dependent information (e.g., TCP, UDP, TLS, HTTP, H.265, etc.), while a private sequence may refer to private and / or proprietary protocol- and format-dependent information, and such information may refer to public and private sequences. In embodiments, a sequence (e.g., a public or private sequence) is computationally transformed into a non-recursive value. While the sequence may be widely heterogeneous (e.g., protocol-independent and have existing entropy), the sequence is processed in a manner that results in a value with a particular level of entropy. In embodiments, this process is compatible with a wide range of protocols and formats, and may begin with different sequences that exhibit their respective existing entropy. In embodiments, this process may be carried out using complex genomic processes and functions that result in genomic engagement factors that exhibit a particular level of entropy.

[0193] In embodiments, CG-based security management systems and architectures may require the use of genome engagement factors in conjunction with genome data constructs. In embodiments, these genome engagement factors may be derived in part through the use of repetitive data (e.g., sequences). Prior to using the sequences in combination with the genome data construct, the sequences are processed so that the entropy of the resulting genome engagement factors matches the entropy of the genome construct (e.g., XNAs). This process is called "sequence mapping," and the product is called a genome engagement factor. Even if the sequences are extensively disjoint, the resulting genome engagement factors exhibit a certain level of entropy. In embodiments, genome engagement factors may be generated from the integration of XNA vectors. In some embodiments, multiple genome engagement factors may be produced from a set of XNA vectors. In some embodiments, this process may be important for open architecture applications that rely on specific digital object transformations, where the objects potentially include heterogeneous protocols and formats (e.g., TCP, UDP, TLS, HTTP, H.265).

[0194] In some embodiments, widely different external formats and protocol-resident data are used to construct sequences without modification. In some embodiments, widely heterogeneous external formats and protocol-resident data are used to construct sequences with modification. In some embodiments, sequences are used in combination with specific genome-based data constructs to determine unique vectors that exhibit specific entropy. In some embodiments, sequences are combined with specific genome data constructs to map according to computationally complex genome processes and functions to derive specific genome engagement factors. In embodiments, sequence mapping derives genome engagement factors that exhibit entropy consistent with the entropy of the genome data construct, regardless of the intrinsic entropy of the sequence. In some embodiments, genome engagement factors may be generated from sequence mapping that utilizes internal CG-ESP formats and protocols in combination with these external formats and protocols. Note that genome engagement factors should be determined in a manner (e.g., using computationally complex functions) that cannot be exploited to reveal the format and protocol-resident data and genome-based constructs.

[0195] In embodiments, sequence mapping performs a genome engagement factor generating genome process computed according to an information-theoretically facilitated computational complexity function. In embodiments, these information-theoretically facilitated functions may be cryptography-based, cryptography-free, or hybrid computational complexity functions, with sequences (public or private) and XNAs generating unique genome engagement factors.

[0196] In some embodiments, CG-ESP may implement a genomic information theory-driven process to facilitate hyperscalable correlation. In embodiments, virtual validation of ecosystem, enclave, and cohort engagement relationships (e.g., unique correlations) can be achieved with hyperscalable correlations. As described, hyperscalability techniques can be used to powerfully enhance ecosystem, enclave, and cohort engagement, which relies on precise and unique correlations. As described, organic ecosystems (e.g., biological ecosystems) demonstrate bounded yet powerful correlations between species, offspring, and siblings that stem from complex biochemical processes. The principles governing these biochemical processes are reflected by specific digital genome structures driven by information theory, which can exhibit unique correlations across ecosystems, enclaves, and cohorts. In embodiments, digital genome correlations are virtually unlimited and exhibit specific, user-controllable entropy. In embodiments, genome eligible objects (eg, CNAs and / or PNAs) and genome correlation objects (eg, LNAs) may be used for digital genome correlation.

[0197] In embodiments, ecosystem VDAXs can utilize computationally complex genomic processes to achieve virtual affiliations with enclaves and cohorts. Similarly, enclave VDAXs may utilize these computationally complex genomic processes to achieve hyperscalable correlations with cohorts, and cohort VDAXs may utilize computationally complex genomic processes to achieve hyperscalable correlations with other cohorts. In embodiments, the inherent hyperscalable correlations between ecosystems, enclaves, and cohorts may be modified by computationally complex genomic processes. For example, an ecosystem VDAX may modify an LNA for a given enclave to prevent future link exchanges in that particular enclave for one or more members of the enclave. In some embodiments, enclave VDAXs and cohort VDAXs that are components of a given ecosystem may employ computationally complex genomic processing to correlate engagements with other ecosystem components, enclave VDAXs and cohort VDAXs. For example, in some embodiments, two ecosystem VDAXs may form derived genomic datasets from their respective genomic datasets, allowing ecosystem members to engage across ecosystems using the derived genomic data (or derivatives thereof). In this way, the enclave VDAX and the cohort VDAX can achieve unique hyper-scalable correlations across multiple ecosystems based on computationally complex genomic processes and their respective genomic datasets. In embodiments, the hyper-scalable correlations perform computational genomic processes according to a wide range of information-theoretically driven computationally complex functions, with PNAs, CNAs, and LNAs generating unique genomic engagement factors. These functions may be cryptographically based, cryptographically free, or hybrid computationally complex functions.

[0198] In some embodiments, CG-ESP may implement processes facilitated by genomic information theory to promote hyperscalable differentiation. In some instances, hyperscalable differentiation may be needed or required to provide unique affiliations between ecosystems, enclaves, and cohorts based on digital network-facilitated relationships. In embodiments, hyperscalability techniques may be used to strongly enforce ecosystem, enclave, and cohort affiliations that depend on precise and unique differentiation. Some exemplary organic ecosystems may show evidence of limited but strong differentiation across species, offspring, and siblings, which may result from complex biochemical processes. The principles governing the biochemical processes in these instances are reflected by specific digital genome structures governed by information theory, which may exhibit unique differentiation across ecosystems, enclaves, and cohorts. In some instances, this digital genome differentiation may be virtually unlimited and may exhibit a specific, user-controllable entropy.

[0199] Various exemplary implementations may exist for applying hyperscalable differentiation in ecosystems, enclaves, and / or cohorts. For example, members of a CG-enabled ecosystem may leverage computationally complex genomic processes to achieve hyperscalable differentiation that promotes unique, non-recursive virtual affiliations between ecosystems, enclaves, and cohorts. In some examples, CG-enabled enclaves leverage computationally complex genomic processes to achieve hyperscalable differentiation that promotes unique, non-recursive virtual affiliations between enclaves and cohorts. In some examples, cohorts may utilize computationally complex genomic processes to achieve hyperscalable differentiation that promotes unique, non-recursive virtual affiliations between cohorts. In embodiments, unique hyperscalable differentiation between ecosystems, enclaves, and cohorts may be altered by computationally complex genomic processes. In some embodiments, enclaves and cohorts that are members of a given ecosystem may employ computationally complex genomic processes to affiliating with enclaves and cohorts that are members of other ecosystems. In this way, enclaves and cohorts may be able to achieve unique virtual affiliations across multiple ecosystems based on computationally complex genomic processes, according to some embodiments of the present disclosure. In some examples, hyperscalable differentiation may execute computational genomic processes according to a wide range of information-theoretically accelerated cryptography-based, cryptography-free, or hybrid (e.g., cryptography-based and / or cryptography-free) computationally complex functions, thereby generating unique genomic engagement factors for sequences and XNAs that can be used to generate VBLS.

[0200] In some embodiments, CG-ESP may implement processes facilitated by genomic information theory to promote virtual agility. In some examples, virtual agility may provide unique engagement between ecosystems, enclaves, and cohorts that may require the ability to perform hyperscalable differentiation and hyperscalable correlation at the network (e.g., Open Systems Interconnection (OSI)), software stack level, and / or hardware components. Both network and software engagement traditionally require the creation, negotiation, and maintenance of session-based protocols. In some examples, these protocols may be computationally expensive and limit network and software stack adoption options. Virtual agility can enhance ecosystem, enclave, and cohort engagement by powerfully eliminating at least some of the requirements for session-based protocols. Virtual agility may reflect a specific digital genome structure, generated by processes facilitated by information theory, that exhibits virtually unlimited, specific, and user-controllable entropy.

[0201] There may be various exemplary implementations for applying virtual agility in ecosystems, enclaves, and / or cohorts. For example, virtual agility may be employed at the network stack level, the software stack level, and / or the hardware level, thereby supporting multiple ecosystems, enclaves, and / or cohorts. In embodiments, virtual agility may eliminate the requirement to create, negotiate, and maintain session-based protocols for network communication engagement, for software application engagement, and / or for hardware component engagement.

[0202] In some embodiments, the CG-ESP may implement a genomic information theory-driven process to generate and / or decode a VBLS. As described, the CG-ESP may be configured to perform link exchange (e.g., link spawning and / or link hosting) and sequence mapping, which may enable computational conversion of digital objects having specific formats and protocols (e.g., packets, sectors, sequences, and frames) into VBLS objects. In embodiments, the VBLS objects generated by this process may be unique, non-repeatable, and / or computationally quantum-proof. In some embodiments, a VBLS may be the culmination of linking, sequencing, correlation, differentiation, and agility functions and processes governed and driven by genomic information theory. A computationally quantum-proof VBLS can form the basis for building specific network, software, and hardware architectures, either in current or newly developed deployments.

[0203] Various exemplary implementations may exist for applying Virtual Binary Language Script (VBLS) in ecosystems, enclaves, and / or cohorts. For example, VBLS may enable extensive and highly flexible control of ecosystem, enclave, and / or cohort relationships. In embodiments, VBLS may facilitate the completion and control of dynamic genome-based architectures. In some examples, VBLS-rendered digital objects may be unique, non-recurring, and computationally quantum-proof, eliminating the need for private key generation, exchange, and retention. VBLS-rendered objects may require minimal overhead and bandwidth for VDAX(s) engagement. In some examples, VBLS-rendered objects may exhibit ecosystem-, enclave-, and / or cohort-oriented genome modifications. In embodiments, VBLS applications may be protocol-agnostic (e.g., interoperable with network, software, and / or hardware solutions). In examples, a VBLS may facilitate unique, non-recurring, computationally quantum proof engagement (e.g., intra-ecosystem, inter-ecosystem, ecosystem-to-cohort, inter-cohort, and / or cohort-to-cohort engagement) between community members based on its unique computationally complex genome construction and processes. In some exemplary embodiments, any VBLS-enabled VDAX can participate in multiple VBLS relationships with other VDAX(s). In these embodiments, a VDAX may form a unique relationship with each VDAX. In some embodiments, the genome engagement factor used for generation may be used simultaneously for primary and secondary applications that also require unique, non-recurring values ​​with a particular entropy.

[0204] In embodiments, a VDAX may be configured to engage in symmetric and / or bi-symmetric VBLS-based engagement. For example, in some embodiments, a VBLS-enabled VDAX(s) may engage based on link exchanges (e.g., spawn and host) that may use the same genome linking instructions and genome structure, resulting in symmetric-based engagement. In embodiments, a VBLS-enabled VDAX may perform bi-symmetric engagement based on highly correlated genome architectures (e.g., identical or other sufficiently correlated XNAs). In these embodiments, a VBLS-enabled VDAX exchanges links that include unique genome regulatory instructions (GRIs). However, in some scenarios, a VBLS-enabled VDAX may perform symmetric engagement if the link exchanges include identical GRIs. For example, a CG-ESP may be configured to perform unidirectional link exchanges, whereby one VDAX provides the GRI used by both VDAXs in the VBLS generation process. In this manner, a VBLS-enabled VDAX can engage with other VDAXs based on a symmetric and / or two-symmetric binary language without repeated coordination between the VDAXs. In some of these embodiments, engagement of a VBLS-enabled VDAX can proceed without formal session negotiation because the symmetric or two-symmetric binary language simultaneously encapsulates authentication, integrity, and privacy.

[0205] The rapid expansion of remote network-centric, highly distributed solutions and services (e.g., remote clouds and edge clouds) has created a situation where sensitive binaries may be executed in open or semi-open environments, exposing them to untrusted third parties (e.g., adversaries). Homomorphic encryption (handling data in encrypted form), functional obfuscation (handling data and application code in encrypted form), and various trusted execution environments (e.g., physical and software isolation of executable code) are current approaches to address these significant exposures. While these methods may provide significant improvements, they all significantly impact performance and therefore do not address the significant scalability required for widespread commercial application.

[0206] According to some embodiments of the present disclosure, CG-ESP genomic information theory technology enables computationally quantum-proof, highly efficient, and hyper-scalable virtual trusted execution domains for processing data and application code, which can be organized as genomic ecosystems. In some of these embodiments, the virtual trusted execution domains enable unique transformations of component-resident executable binaries and data, such as applications (e.g., APIs, libraries, and threads), operating systems (e.g., kernels, services, drivers, and libraries), and systems-on-chip (e.g., processing units, e.g., cores). In embodiments, CG-ESP executable isolation components (EICs) facilitate the component-binary-isolation (CBI) necessary for the necessary transformations. In some embodiments, isolation is enabled by 1) inherent genomic correlation between distributed components belonging to the same ecosystem and 2) inherent genomic differentiation from other executable ecosystem components. This process of correlation and differentiation forms the basis for virtual trusted execution domains (VTEDs) enabling highly flexible and scalable component-binary-isolation (CBI).

[0207] In embodiments, hyperscalable differentiation enables highly flexible component binary isolation (CBI) of ecosystems, enclaves, and cohorts. In some of these embodiments, isolation of Virtual Trusted Execution Domains (VTEDs) may be achieved through the sharing of genome components within VTEDs and their CBIs with VTED ecosystem VDAXs, thereby establishing a hierarchical link between these members. In embodiments, VTEDs provide a functional alternative to homomorphic encryption, where CBIs are held at rest and runtime operations are performed on encoded binaries and associated data.

[0208] In embodiments, VTED virtual agility enables highly flexible component binary isolation (CBI) and control of dynamic genome-based architectures. In further embodiments, genome correlation and differentiation enables dynamic genome-based systems to configure dynamic genome network topologies without the need to modify the physical operating environment.

[0209] In embodiments, the VTED-transformed executable binary is unique, non-reproducible, and computationally quantum-proof. In embodiments, this transformation eliminates the requirement for secret key generation, exchange, and maintenance often required by trusted execution environment (TEE) technologies. In embodiments, the VTED-executable binary is transformed through the application of a genome construct (e.g., LNA or ZNA) to construct the transformed executable binary.

[0210] In embodiments, VTED's hyperscalable correlation, hyperscalable differentiation, and hyperscalable agility uniquely enable CBIs to operate with minimal overhead and bandwidth. In further embodiments, multiple genome architectures are applied to a vast number of CBIs, providing the hyperscalability of the VTED ecosystem.

[0211] In embodiments, VTED-enabled CBIs for ecosystems, enclaves, and cohorts may be directly genomically modified without compromising binary executable relationships. In embodiments, VTED-enabled CBIs modify binary information while maintaining the ability of VTED to execute CBIs within its ecosystem.

[0212] In embodiments, VTED-enabled CBIs may be compatible with known cryptography-based and cryptography-less computational methods. In further embodiments, compatibility of VTED and CBIs with cryptography-based and cryptography-less computational methods is maintained through transparent genome assembly-based transformations.

[0213] In embodiments, VTED may enable unique, non-recurring, computationally quantum-proof CBI feasibility among specific ecosystems based on their unique computationally complex genome architecture and processes. In further embodiments, the CBI construction process applies genome architecture that does not rely on traditional computationally expensive operations.

[0214] In embodiments, VTED may enable CBI executables to have many properties, including unique, non-recurring, computationally quantum proof engagement between a particular ecosystem and enclave. In embodiments, CBI executables exhibit these properties based on their unique computationally complex genome architecture and processes. In further embodiments, VTED applies genome architecture to deploy CBI executables that enable quantum proof operations between the CBI executable and the genome VTED.

[0215] In embodiments, VTED can enable CBI executables based on their unique, computationally complex genome structures and processes. In further embodiments, in scenarios where VTED includes multiple cohorts across multiple enclaves, VTED can apply genome components to enable CBI executables that may have certain desirable properties, such as being unique across entities, non-reproducible, quantum-proof, etc. In further embodiments, Ecosystem VDAX can provide a genome-construction-based CBI licensing model in which individual cohorts can have specific features or CBIs enabled for operation within that ecosystem, enclave, or cohort.

[0216] Referring now to FIG. 6 , an exemplary CG-enabled digital ecosystem 600 is depicted in accordance with some exemplary embodiments of the present disclosure. Note that the example configuration of the CG-enabled digital ecosystem 600 depicted in FIG. 6 , including the topography and architecture of the security platform depicted therein, is provided as a non-limiting example and is not intended to limit the scope of the present disclosure. As discussed throughout this disclosure, the configuration of the CG-ESP may be defined by a community owner of the digital ecosystem. When referring to a “community owner” of a digital ecosystem, this term may refer to an entity that manages, maintains, or owns the community, its representative (e.g., a network administrator, a CIO, an IT administrator, a landlord, a consultant, a security expert, artificial intelligence software acting on behalf of the community owner, or any other suitable representative), and / or any other appropriate party that may define the configuration of the CG-ESP used in connection with the CG-enabled ecosystem 600.

[0217] In an embodiment, a set of VDAXs (e.g., VDAXs 608, 610, 612, 614) perform a set of genome security functions on behalf of the digital ecosystem 600. Note that a VDAX may also be referred to as a "CG-Security Controller" or a "Security Controller." In an embodiment, the CG-enabled digital ecosystem 600 includes a set of enclaves 602 and, for each enclave, a respective set of cohorts. It is noted that a general reference to a CG-ESP may be a reference to the configuration of VDAXs (e.g., ecosystem VDAX 608, enclave VDAX 610, cohort VDAX 612, and / or subordinate VDAX 614) participating in the digital ecosystem. In an embodiment, the set of cohorts may include an independent cohort 604. As described, an independent cohort 604 may include a collection of one or more devices operating as an independent entity. Examples of independent cohorts 604 include, but are not limited to, grids, networks, cloud services, systems, computers, home appliances, devices, IoT devices, etc. In some embodiments, the set of cohorts may further include dependent cohorts 606. A dependent cohort 606 may refer to an individual digital entity realized by the digital container-based VDAX or for which the independent cohort acts as a representative. Examples of dependent cohorts include, but are not limited to, sensors, applications, data, files, databases, media content, cryptocurrencies, smart contracts, etc. An enclave 604 may be a collection of two or more cohorts (e.g., independent cohorts 604 and / or dependent cohorts 606) that have a mutual identity of interest. As described, the mutual identity of interest may be any logical commonality between cohorts within the enclave. For example, the mutual identity of interest may be a collection of devices, servers, documents, applications, etc. used by a business unit within an enterprise organization. In another example, the mutual identities of interest may be devices, documents, applications, etc. that belong to a single family or user.In another example, the mutual identities of interest may be a set of autonomous vehicles traveling on a particular grid. In embodiments, the digital community's topography (and the corresponding CG-ESP's architecture) may be defined by a community owner with these mutual identities of interest in mind. In embodiments, eligibility for membership in ecosystem 600 and / or one or more of its enclaves 602 may be defined by the community owner, and membership and revocation may be managed by the community owner and / or according to one or more sets of rules. Note that in some embodiments, the respective architectures of a particular CG-enabled digital ecosystem and corresponding CG-ESP may be defined according to a default configuration, such that a community owner purchases or otherwise obtains a digital ecosystem pre-configured with a default configuration.

[0218] In some embodiments, ecosystem descendants (e.g., ecosystem VDAX 608) may be configured to build one or more enclaves 602 and populate each enclave 602 with a respective set of cohorts according to the architecture and configuration defined by the community owner. In some embodiments, the architecture and configuration for the CG-enabled digital ecosystem 600 may be defined by a CG-ESP (e.g., as described in FIG. 4). In these embodiments, VDAXs participating in the digital ecosystem 100 may each run a respective instance of a CG-ESP, where the CG-ESP instance enables each VDAX to fulfill its respective role with respect to the ecosystem 600 and form relationships with its intended ecosystem members. For example, a set of VDAXs may include any suitable combination of an ecosystem VDAX 608 fulfilling an ecosystem-level role, one or more enclave VDAXs 610 fulfilling an enclave-level role, one or more cohort VDAXs 612 fulfilling a cohort-level role, and / or one or more subordinate VDAXs 614 fulfilling subordinate cohort roles. For example, in some example implementations, ecosystem VDAX 608 may be configured (e.g., via a CG-ESP instance) to generate, assign, and persistently modify genomic data for other ecosystem members, verify engagement eligibility, exchange links, and generate VBLS; whereas cohort VDAX 612 (e.g., via a cohort CG-ESP instance) may not have the ability to create or assign genomic data for the ecosystem to other cohorts, but is configured to verify engagement eligibility, exchange links, and generate VBLS.

[0219] In embodiments, VDAX may be implemented as any combination of software, hardware, firmware, and / or middleware that performs a specific set of genomic functions with respect to the ecosystem. Note that the existence of a dependent cohort 606 depends on at least one independent cohort (e.g., a file depends on the device on which it is stored, or an application instance depends on the device on which the application runs). Thus, in some embodiments, the dependent VDAX 614 of a dependent cohort 606 (e.g., a file, media content, application, etc.) may be executed by an independent cohort 604 (e.g., a user device, smart device, gaming device, personal computing device, server, cloud system, etc.) on which the dependent cohort 606 depends.

[0220] In embodiments, Ecosystem VDAX 608 may be considered the "ancestor" of the ecosystem because it performs security-related functions for the digital ecosystem and does not require any subsequent interaction with the enclave or its cohorts after Ecosystem VDAX 608 initializes and assigns the enclave its genomic dataset. Note that in embodiments, an ecosystem may be configured to allow for independent sub-ecosystems, which have functional ecosystem-level VDAX capabilities but may include multiple lower-level VDAXs that derive from the primary Ecosystem VDAX 608.

[0221] In embodiments, ecosystem VDAX 608 digitally generates each genomic dataset for one-time distribution to the ecosystem's enclaves 602 and / or cohorts 604, 606 within each enclave 602. Genomic data objects within a genomic dataset can have similar or identical structures, mathematical capabilities, and / or entropy levels, but each serves a different purpose. In embodiments, genomic eligibility objects (e.g., CNA or PNA objects) provide the core genomic capabilities by which community members (e.g., enclaves or cohorts) computationally correlate individual ecosystem identities. In embodiments, genomic correlation objects (e.g., LNA objects) provide the capability for link exchange between members (e.g., ecosystem-to-enclave, enclave-to-enclave, enclave-to-cohort, cohort-to-cohort, etc.), which governs a member's ability to establish engagement with another member. In embodiments, genome differentiation objects (e.g., XNA or ZNA objects) provide the capability for two-way symmetric communication between members based on VBLS. In embodiments, the digital genome structures of CNAs, PNAs, LNAs, and XNAs are complex and unique. In embodiments, CNAs, LNAs, XNAs, and PNAs may be derived using complex mathematical functions.

[0222] According to some embodiments of the present disclosure, Ecosystem VDAX 608 may generate a genomic dataset that it assigns to itself. The genomic dataset may include one or more different types of genomic data objects. For example, in some embodiments, Ecosystem VDAX may generate genomic eligibility objects (e.g., CNA objects and / or PNA objects), genomic correlation objects (e.g., LNA objects), and genomic differentiation objects (e.g., XNA objects or ZNA objects) according to platform instance requirements (e.g., the type of genomic object, the entropy level of each genomic object, and the particular algorithm used to generate such genomic data objects). In embodiments, the genomic dataset initially generated by Ecosystem VDAX 608 and assigned to the entire ecosystem 600 may be the genomic dataset from which all descendant genomic datasets of the digital ecosystem 600 derive. For ease of description, the genomic dataset of an ecosystem's descendants may be referred to as a "descendant genomic dataset" (or "descendant DNA set"). In some embodiments, Ecosystem VDAX 608 may initially generate an ancestral genomic dataset. For example, ecosystem VDAX 608 may generate, for each ancestral genome data object, a respective binary vector having a particular dimension.

[0223] In some embodiments, ecosystem VDAX 608 may generate a respective descendant genome dataset for each enclave from the ancestral genome dataset. In some embodiments, ecosystem VDAX 608 may modify the ancestral genome dataset using a predefined set of genome operations to obtain descendant genome datasets (or "enclave datasets") that are then propagated to each enclave. In some embodiments, ecosystem VDAX 608 may generate a respective descendant genome dataset for each enclave from the ancestral genome dataset. In some embodiments, ecosystem VDAX 608 may modify the descendant genome dataset using a predefined set. For example, ecosystem VDAX 608 may modify the descendant genome eligibility objects of the descendant genome dataset using a computationally complex function to obtain a different enclave genome eligibility object for each enclave in the ecosystem. The first and second methods involve modifying the ancestor correlation object of the ancestral genome dataset using a computationally complex function to obtain a different enclave correlation object for each enclave in the ecosystem, and modifying the ancestor differentiation object of the ancestral genome dataset using a computationally complex function to obtain a different enclave differentiation object for each enclave in the ecosystem. In embodiments, the techniques by which different types of genome objects are modified may differ because different types of genome objects may be implemented with different types of data structures and / or may be required to exhibit different characteristics. Different modification techniques are described throughout this disclosure. Note that in some implementations of the security platform, only a single enclave may exist. Depending on the various techniques implemented in a particular CG-ESP, certain types of genome objects (e.g., LNAs and XNAs) may be highly correlated (e.g., identical or otherwise sufficiently correlated) with some or all enclaves, while other types of genome objects (e.g., CNAs or PNAs) may be unique to each respective community member but still sufficiently correlated.Even if some types of genomic objects in the descendant genomic dataset are not modified from the corresponding genomic objects in the descendant genomic dataset, the modification of one or more other portions of the genomic dataset and the subsequent assignment of the descendant genomic dataset to descendant community members (e.g., enclave, or cohort) may also be referred to as "derived," such that the descendant genomic dataset (e.g., enclave genomic dataset or cohort genomic dataset) can be said to be derived from the descendant genomic dataset (e.g., descendant genomic dataset or enclave genomic dataset) even if one or more genomic objects in the descendant genomic dataset were not modified from the descendant genomic dataset.

[0224] In embodiments, enclave VDAX 610 may be configured to add cohorts to a corresponding enclave by modifying the enclave's enclave genomic dataset and assigning the resulting progeny genomic dataset to each cohort within the enclave. In some embodiments, enclave VDAX 610 may generate a cohort genomic dataset for each new independent cohort 604 added to enclave 602. In some embodiments, CG-ESP may be configured such that enclave VDAX 610 generates unique, yet highly correlated, genomic eligible objects (e.g., CNAs) for each independent cohort 604 that has been added or will be added to the corresponding enclave 602. In some of these embodiments, ecosystem VDAX 608 or enclave VDAX 610 may generate genomic eligible objects such that any set of cohorts within an enclave has a unique correlation of genomic eligible objects. For example, in some embodiments, each cohort of enclave 602 is assigned a genomic eligibility object that is generated based on the genomic eligibility of the objects of its descendants (e.g., ecosystems or enclaves) so that the cohorts are unique while maintaining a high level of correlation. In this manner, any pair of cohorts can confirm eligibility to engage with each other based on the correlation of their respective genomic eligibility objects. In some embodiments, members of an enclave (e.g., cohort VDAX) are assigned highly correlated (e.g., identical or otherwise sufficiently correlated) genomic correlation objects and genomic differentiation objects. In some embodiments, pairs of cohorts may authenticate each other based on each cohort's respective genomic correlation object and may be distinguished from other cohorts based on each cohort's respective genomic differentiation object. In embodiments, a cohort's genomic correlation object and genomic differentiation object may be separate objects (although they may be similar or identical in structure).Alternatively, in some embodiments, the genomic correlation object and the genomic differentiation object for a cohort may be the same object.

[0225] Note that while in some embodiments, a cohort genomic dataset is assigned to only one entity (e.g., a device, a document, a sensor, etc.), in other embodiments, a community owner may allow the cloning of a cohort's genomic dataset to one or more additional community members. For example, a user may have two devices (e.g., a desktop and a laptop computer) that they use in connection with work. The community owner may choose to assign identical copies of the genomic dataset to the devices in this scenario. In this manner, each device associated with the user may be granted identical access rights with respect to their respective enclave 602. Note that in some of these embodiments, each respective device with a cloned genomic dataset would still need to independently qualify, authenticate, and / or exchange links with other cohorts in the enclave 602.

[0226] It is noted that in some embodiments, when a VDAX is assigned a genomic dataset and added to the digital ecosystem 600, the VDAX may also receive configuration data (e.g., as defined in the CG-ESP instance) as well as other appropriate data that may be required to participate in the ecosystem. Such configuration data may enable VDAX to use the correct genomic functions when performing genomic operations such as eligibility correlation, link spawning, link hosting, sequence mapping, LNA correction, XNA correction, binary object conversion, etc. In these embodiments, such configuration data enables community members to successfully engage and exchange data with other community members. In some embodiments, the VDAX may also receive genomic community progeny (GCP) data that uniquely identifies community members. In these embodiments, the GCP may be used in confirming engagement eligibility of a cohort.

[0227] In some embodiments, cohort VDAX 612 may be configured to perform genome security operations and processing on behalf of independent cohorts 604. In some embodiments, cohort VDAX 612 facilitates data exchange with sufficiently correlated community members (e.g., other cohorts in enclave 602). In some of these embodiments, facilitating data exchange with another community member may include verifying engagement eligibility (e.g., engagement completeness and engagement synchronization) and exchanging links with the other respective community members (e.g., with another independent cohort 602). In some embodiments, verifying engagement eligibility and link exchange is a one-time process so that once a pair of cohorts successfully completes this "handshake," the VDAX pair can securely exchange data as long as they continue to share highly correlated (e.g., the same or other sufficiently correlated) differential objects. For example, a pair of VDAXs may initially verify their eligibility for engagement, exchange links, and continue to communicate securely for days, weeks, months, or even years, provided they no longer share a common differentiating object. Once they no longer share a common differentiating object, they will no longer be able to decrypt any encoded digital objects provided by their respective cohorts, although they may still be able to attempt data exchange.

[0228] In embodiments, a pair of VDAXs engage with each other through a virtual binary language script (VBLS) generated and decoded by each VDAX. As described, VBLS may refer to a unique, non-recurring (or infinitesimally recurring) binary language. In embodiments, an individual instance of a VBLS may be referred to as a VBLS object. In embodiments, a first VDAX (e.g., cohort VDAX 612 or enclave VDAX 610) may generate a VBLS object for a second VDAX (e.g., cohort VDAX 612 or enclave VDAX 610) based on the genomic regulatory instructions (GRIs) encoded in a link provided by the second VDAX to the first VDAX and the first VDAX's genomic data (e.g., XNAs). In these embodiments, the second VDAX may receive a VBLS object from the first VDAX and decode the VBLS based on the GRI and the VDAX's genome dataset provided in the link to the first VDAX. In some embodiments, the VBLS object includes metadata that the second VDAX processes to decode the encoded digital object included in the VBLS object. For example, in some embodiments, the VBLS object is a data packet that includes a packet header and the encoded digital object (e.g., payload). In some of these embodiments, the metadata used to decode the encoded digital object includes public or private sequences that appear in one or more protocol layers of the digital object (e.g., TCP, UDP, TLS, HTTP, H.256, or any other suitable protocol layer type).

[0229] In an embodiment, the first VDAX can generate a VBLS object corresponding to the digital object to be provided to the second digital object by determining a genome engagement factor based on the sequence (e.g., a public sequence or a private sequence) and the first VDAX's genome differentiation object. In an embodiment, the first VDAX modifies its genome differentiation object according to the GRI provided by the second VDAX at the link provided by the second VDAX, and maps a sequence (or a value derived therefrom) included in the digital object (e.g., protocol or format data within the digital object) to the modified genome differentiation object to obtain a genome engagement factor. In an embodiment, the first VDAX may map the sequence to the modified genome differentiation object using a computationally complex function (e.g., a cryptographic function, a non-cryptographic function, or a hybrid function). The first VDAX may then encode the digital object (e.g., a packet payload, a shard of a file, a video or audio frame, or any other suitable type of digital object) using the genome engagement factor to obtain an encoded digital object. In an embodiment, the first VDAX utilizes a computationally complex function (e.g., an encryption function or a disambiguation / XOR function) to encode the digital object based on the genomic engagement factor. The first VDAX can then provide a VBLS object containing the metadata (e.g., sequence) and the encoded digital object to the second VDAX (e.g., via a network and / or data bus).

[0230] In an embodiment, the second VDAX may receive the VBLS object and decode the encoded digital object in the VBLS object based on the metadata included in the VBLS object and the second VDAX's genome identification object. In an embodiment, the second VDAX is configured to extract a sequence from the VBLS object (e.g., an unencrypted portion of a public or private sequence of a data packet or data frame). The second VDAX may also modify its genome differentiation object using the GRI included in the link provided to the first VDAX (e.g., during the link exchange process) so that the second VDAX can map the sequence (or a value derived therefrom) to the modified genome differentiation object using the same computational complexity function to obtain a genome engagement factor. Assuming the first and second VDAX have matching (or in some embodiments sufficiently correlated) genome differentiation objects and both use the same instructions to modify their respective genome differentiation objects, the same genome engagement factor will be generated given the same sequence and modified genome differentiation object. In embodiments, the second VDAX utilizes a function (e.g., a decryption function or a disambiguation / XOR function) to decrypt the digital object based on the same genome engagement factor. In this manner, the first VDAX and the second VDAX can uniquely differentiate from other community members who share the same genome identification object because other community members who do not own the link that the second VDAX provided to the first VDAX cannot modify the genome identification object in the same way. Therefore, other community members will not be able to generate the genome engagement factor, even if those community members are configured to perform the same computationally complex mapping function and are able to determine the public sequence.Intruders or other malicious devices without access to genomic data would be further limited because they may not know the computationally complex functions used to generate the genomic engagement factor, how to extract sequences, or one or more common genomic differentiation targets. Therefore, the genomic engagement factor can only be determined by brute force. Furthermore, because CG-ESP can be configured such that a first VDAX calculates a new genomic engagement factor for every digital object (e.g., every data packet, file shard, video frame, audio frame, etc.), each encoded VBLS object would require a separate brute force test of the digital object, making the VBLS generated by the first VDAX quantum-defensible against the second VDAX.

[0231] In some embodiments, the metadata of the VBLS object may further include data integrity information. For example, the data integrity information may be a value calculated by a first VDAX on the plain data and then used as a sequence. In this way, a second VDAX can verify that the VBLS object has not been tampered with.

[0232] In embodiments, digital objects may refer to OSI components (e.g., Level 2-7 components) and / or computer-executable code / instructions. Examples of digital objects include packets, sectors, frames, and sequences. A VBLS may refer to a language spoken by one enclave or cohort to another enclave or cohort, uniquely understood by the recipient enclave or cohort, i.e., a language only the recipient enclave or cohort can understand. This prevents intruders targeting rogue cohorts, viruses, malware, etc. from generating or deciphering VBLS among legitimate cohorts.

[0233] It is understood that the foregoing description is provided as one example of a CG-enabled digital ecosystem 600. It is understood that different configurations of CG-ESP may perform different functions and operations and may have different CG-ESP modules. For example, different configurations of CG-ESP may use different encryption functions, different hash functions, different sequence mapping functions, different types of genome construction, or the like. It is further noted that CG-ESP may be configured for different ecosystems that may enable different architectures.

[0234] Figures 7-11 illustrate different types of digital ecosystems and corresponding architectures. Modern network capabilities substantially reflect their underlying deployment architectures. In embodiments, CG-enabled architectures that enable VBLS using genome structures operate at the bit level and can therefore maintain interoperability with the underlying deployment architecture. VBLS provides unprecedented facilities and flexibility to uniquely tailor applications for network, software, and / or hardware-centric architectures. Example architectures for CG-ESP ecosystems include, but are not limited to: architectures that support static ecosystems; free-form architectures configured for transient ecosystems; spontaneous architectures that support dynamic ecosystems; ephemeral architectures that support executable ecosystems; and interledger architectures that support affirmative ecosystems. In embodiments, these architectures, which may overlay existing physical network topologies, demonstrate genome-built topologies. In some embodiments, multiple genome-built topologies may exist simultaneously and interoperably. For example, a computing device may be a viable ecosystem in which internal components of the computing device exchange VBLS, and at the same time, the computing device may be a member of a static ecosystem in which it may engage with other devices in the static ecosystem using different sets of genomic data.

[0235] Referring to Figure 7, directed architectures can be implemented in static ecosystems because the characteristics of such ecosystems, and the enclaves and cohorts participating in these ecosystems, exhibit fairly stable configurations. For example, in an enterprise deployment, the majority of users use similar devices (e.g., desktops, laptops, and mobile devices), email clients, software solutions (both cloud-based and locally executed), devices (e.g., printers, IoT devices), etc., all of which may not change significantly over time. These ecosystems provide stable relationships without losing flexibility or encumbrance. These architectural attributes are independently extended and strengthened—so that they can stand alone. In some exemplary embodiments, a provision that can be enabled by a static architecture, as opposed to a free-form architecture, may be the way correlations are performed and managed. In a static architecture, correlations are achieved based on a common genome structure provided by a single ecosystem, VDAX.

[0236] In embodiments, a directed architecture reflects a configuration in which an ecosystem VDAX establishes one or more enclaves that exhibit specific genomic correlations and differentiation. In some of these embodiments, each enclave VDAX may correspondingly establish one or more cohorts that also exhibit specific genomic correlations and differentiation. In embodiments, such ecosystem, enclave, and cohort configurations may exhibit hierarchical genomic correlations and differentiation, which may be beneficial in a directed architecture. In embodiments, directed architectures, ecosystems, enclaves, and cohort VDAXs may have multiple genomic correlation and differentiation attributes. For example, an enclave in a directed architecture may propagate both sub-enclaves and cohorts. In embodiments, different architectures may be configured to exhibit different correlation characteristics. For example, a directed architecture may exhibit inherently common correlations, while a free-form architecture may exhibit arranged common correlations.

[0237] In embodiments, genomic correlation and differentiation allows for the configuration of genomic network topologies in a directed architecture without the need to change the physical topology. For example, in these embodiments, a community owner may be able to control the engagement of cohorts in different enclaves with different LNAs and XNAs, such that engagement (e.g., via link exchange) between cohorts in different enclaves can be prevented by the community by controlling the LNAs and / or XNAs provided to different enclave members. Similarly, in these examples, a community owner can create new enclaves by controlling the LNAs and XNAs provided to different cohorts.

[0238] In embodiments, the genome topology enabled by the directed architecture may be incrementally modified. In some of these embodiments, a community owner may periodically modify the specific genome architectures (e.g., XNAs and / or LNAs) of some or all ecosystem and / or enclave members for any number of considerations (e.g., security, removal of no longer-existing cohorts, enclave dissolution, etc.).

[0239] FIG. 7 illustrates an example of a CG-enabled ecosystem 700 with a directional architecture, whereby the ecosystem 700 is a static ecosystem. In an embodiment, the static ecosystem includes enclaves and cohorts that support more traditional deployments, including local, internally managed but distributed, and remote on-demand IT resources and capabilities. Static ecosystems demand seamless performance and security. While such deployments are common in enterprise-class organizations and institutions, security is challenging for small and medium-sized businesses (SMBs) due to their complexity. These implementations tend to be relatively static and centrally managed. For example, in a static ecosystem, a business unit may contain many employees who are granted access (e.g., read, write, and / or edit) to a common set of files. Additionally, employees may work on special projects (e.g., product releases), and these employees are typically granted access to another common set of files. In some scenarios, a community owner (e.g., represented by an IT administrator or any other party affiliated with an enterprise) may define a set of policies that define the types of access that individual cohorts may be granted with respect to particular files or folders, the enclave or enclaves to which each cohort belongs, the cohorts and / or enclaves (printers, local file servers, etc.) with which each cohort may digitally engage, and / or other appropriate policies. As described, such policies may be implemented using genome constructs such as XNAs, CNAs, PNAs, and LNAs, which may be used to define allowable relationships and genome topologies across the ecosystem.

[0240] In an embodiment, when the digital ecosystem is a static ecosystem 700, the security platform may be implemented as a directional architecture. Using a directional architecture, an ecosystem VDAX (e.g., a private VDAX 702) defines one or more enclaves 704 corresponding to the static ecosystem. In the example of FIG. 7 , the ecosystem VDAX 702 hierarchically defines N enclaves 704, including a first enclave 704-1 and an Nth enclave 704-N (e.g., a directional architecture). For each enclave 704-1...704-N, the ecosystem VDAX 702 can create an enclave VDAX (that runs the enclave VDAX) for the enclave 704-1, and can assign one or more cohorts 706 to the enclave 704-1. In this example, the cohorts 706 of the first enclave 704-1 and the Nth enclave 704-N include workstations, tablets, local data centers, printers, IoT devices, mobile devices, and the like. Note that in this example, the routers in each enclave are not considered cohorts and do not communicate using VBLS. Rather, each router is a pass-through device that routes data packets containing VBLS to the cohorts 706 within the enclave 704, within the ecosystem 700, and / or to any wider network (e.g., the Internet). In embodiments in which the routers are cohorts, each router may have its own genomic data set (XNA, LNA, and CNA), and the other cohorts 706 within the enclave 704 will communicate with the router using VBLS that only the router understands. It is understood that such a decision is a design choice that can be made by the community owner or the provider of the CG-ESP. Additionally, it is noted that the cloud facility 710 in this example is not an enclave of the ecosystem 700. In this example, cloud facility 710 hosts third party applications and / or data.In some exemplary embodiments, ecosystem VDAX 702 of ecosystem 700 may be configured to negotiate arrangements with third-party application systems and / or cloud facilities (not shown) to obtain genomic material correlated to designated ecosystem cohorts, thereby enabling authentication, linkage, and engagement between the ecosystem and the third-party application systems / cloud facilities. Additionally or alternatively, a community owner may decide to add specific third-party service providers (e.g., cloud services) as cohorts to the ecosystem, allowing the community owner to restrict the third-party service provider's access to the ecosystem to specific uses via LNA and XNA construction. In this way, the third-party service will only be able to exchange links with other cohorts (e.g., intended users of the third-party service) that have similar LNAs. Similarly, if the relationship with a third-party service provider is terminated, the community owner can revoke the third-party service provider through XNA modification.

[0241] In some embodiments, an enclave VDAX (or ecosystem VDAX) of an enclave 704 can generate and assign genomic material to the VDAXs of each cohort 706 of the enclave 704. In embodiments, the ecosystem VDAX 702 creates genomic information (e.g., XNAs, LNAs, and CNAs) for each enclave. In response to receiving that genomic information, the enclave VDAX may generate respective genomic information for each cohort 706 included in the enclave 704. For example, the enclave VDAX (or ecosystem VDAX) may generate CNAs to be assigned to a new cohort and / or provide the LNAs and / or XNAs to members of the enclave 704. Depending on the configuration of the CG-ESP and its genomic structure, two cohort VDAXs enrolled in an enclave may need to participate in a link exchange. Once two VDAXs have participated in a link exchange, they can begin exchanging VBLS based on the hosted link.

[0242] In a directed architecture, an ecosystem owner (e.g., via ecosystem VDAX 702) can manage the security functionality of a cohort 706 within an enclave 704 by initiating modification of the XNAs and / or LNAs of the cohort 706 and / or enclave 704. For example, if an employee is no longer part of a business unit, that employee's access to certain resources (e.g., documents, printers, file systems, etc.) may be revoked. In embodiments, VDAX may "revoke" an employee's access to a cohort (e.g., workstations, mobile devices, etc.) by initiating modification of the XNAs (and in some scenarios, LNAs) of the enclave 704 and / or cohorts 706 remaining in ecosystem 700, without initiating the same XNA modification for the cohort 706 corresponding to the removed employee. In another example, if an employee has access to a first folder of documents and a second folder of documents, and the employee's access to the first folder is revoked but not to the second folder, VDAX may undertake to modify the XNAs in the second folder and the DNA and LNAs of other cohorts in the enclave without providing the modifications to the cohort(s) of the employee whose access to the second folder was revoked. In these provided examples, the community owner can use genome constructs (e.g., LNAs and / or XNAs) to control the engagement of cohorts in different enclaves.

[0243] It is understood that the foregoing description provides some example implementations of a directed architecture, and that CG-ESP may be configured according to a directed architecture in other suitable ecosystems without departing from the scope of this disclosure.

[0244] Referring now to FIG. 8, a free-form architecture features potentially unrelated ecosystems, enclaves, and cohorts. In such an ecosystem, the composition of enclaves (e.g., limited number of users, devices, etc.) is fairly stable but may change according to mutual interests (e.g., changes to the ecosystem are less predictable than in a static ecosystem). Thus, a free-form architecture may provide stability of relationships without losing or inhibiting flexibility. In some embodiments, these architectural attributes may be independently extended and enhanced—such an architecture is capable of standing alone. In embodiments, a provision that a free-form architecture, as opposed to a static architecture, may enable is the way correlations are performed and managed. In the case of a free-form architecture, common genome correlations cannot be achieved on the same basis as in a static architecture; such is achieved by using alternative genome substructures to facilitate common genome correlations, e.g., free-form.

[0245] In embodiments, the free-form architecture may facilitate genome construction of application-specific network topologies. For example, in some embodiments, an ecosystem VDAX independently initiates its own genome correlation and / or differentiation construction (e.g., LNA and / or XNA). In some embodiments, enclave VDAXs and / or cohort VDAXs in a directed architecture may directly acquire their unique genome correlation constructions (e.g., LNA). For example, an ecosystem VDAX may control which cohorts belong to which enclaves through the generation and modification of genome constructions.

[0246] In embodiments, ecosystem VDAXs in a free-form architecture may acquire their unique genome correlation structure through alternative genome substructures. In some of these embodiments, each ecosystem VDAX may have a unique genome structure from which correlation and differentiation attributes are derived. In some of these embodiments, these derived attributes control the topology of the genomes of the ecosystem's enclaves. In some of these embodiments, each enclave (e.g., through its enclave VDAX) may have a unique genome architecture from which correlation and differentiation attributes are derived, and these attributes control the genome topology of the enclave's cohorts (e.g., cohort VDAXs). In embodiments, multiple genome architecture topologies overlaying a physical network topology can exist simultaneously and in an interoperable manner. In some embodiments, the genome-based digital network topology is independent of the underlying technology used to enable the physical or logical digital network. In these embodiments, the genome-based digital network renders its topology, which may in some scenarios rely solely on genome architecture-facilitated VBLS.

[0247] 8 illustrates an example of a security platform with a free-form architecture that provides services to an interaction ecosystem 800. In an embodiment, the interaction ecosystem 800 may include one or more enclaves 804 (e.g., a personal residence, a home office, a small business, etc.), each of which allows one or more cohorts 806 (e.g., computers, appliances, hubs, media devices, IoT devices, wearable devices, smart speakers, etc.) to identify themselves to one another and interact with a wide range of network-enabled web portals (e.g., Facebook, Amazon, bank servers, healthcare servers, etc.) where services and applications are interactive but require user-controlled security. An example of an interaction ecosystem 800 may be a home network, a small office network, etc.

[0248] In a free-form architecture, a cohort 806 within ecosystem 800 may be designated as a VDAX 802 of ecosystem 800. For example, a user may designate a mobile device, desktop, or router to function as a VDAX 802 of ecosystem 800. Additionally, through VDAX 802, a user may define (e.g., via a user interface) one or more enclaves. For example, in some situations, a user may define a single enclave 804 (e.g., all devices associated with the user). In another example, a user may define different enclaves 804 for different family members, different device classes, and / or other logical commonalities (e.g., an enclave for devices used by parents, an enclave for devices used by minors, and an enclave for smart devices such as thermostats, appliances, televisions, speakers, etc.). In an embodiment, a user may define one or more settings (e.g., rules, policies, blacklists, whitelists, etc.) for enclaves 804 and / or individual devices through VDAX 802. These parameters may be used in generating genomic data (e.g., XNAs, LNAs, CNAs, and / or PNAs) for an enclave 804 or a cohort 806. VDAX 802 may generate genomic data for each enclave 804. In some embodiments, VDAX 802 may also generate genomic data for each cohort (independent and dependent). In other embodiments, a separate device may host the enclave VDAX, whereby the enclave VDAX generates the XNAs, LNAs, PNAs, and / or CNAs for the cohort 806 within the enclave 804.

[0249] In interactive ecosystem 800, the external systems that cohort 806 may access are widespread. For example, a user may use a workstation or mobile device to access a web portal to stream video, access social media platforms, visit websites, read emails and messages, open attachments, and so on. Similarly, a user may have devices in their home that can record motion detection, audio recording, video capture, sensor measurements, or other data related to the user and their home or office. These devices may also access a web portal to report data or utilize the web portal's services (e.g., ordering products, adjusting a thermostat, etc.). In the former example, the user may be concerned about privacy and / or malicious software (e.g., viruses or malware) being installed on the device. In the latter example, the user may be concerned about privacy (e.g., who can access data captured by their smart device or unknown surveillance). In interactive ecosystem 800, a security platform implemented as a free-form architecture mitigates these concerns. In some embodiments, a VDAX 802 of ecosystem 800 may negotiate a secure relationship with a VDAX (not shown) of portal 810. In some of these embodiments, the user and web portal VDAX generate correlated genomic data. In these embodiments, the VDAX 802 of the interactive ecosystem may then generate genomic data for a cohort (e.g., by the cohort's VDAX) attempting to access the web portal 810. When a cohort 806 attempts to access the web portal, the cohort 806 and web portal 810 generate and exchange engagement information that allows the corresponding VDAX pair to verify eligibility-completeness and / or synchronization and ultimately exchange links. Once the web portal 810 and cohort 806 have generated and exchanged links, the cohort 806 and web portal 810 may each host the other's link.Cohort 806 may use links generated and hosted by web portal 810 to generate VBLS that are sent to web portal 810, and web portal 810 may use links generated by cohort 806 to generate VBLS that are sent to cohort 806. The foregoing example is one example of a free-form architecture, and other implementations are within the scope of this disclosure.

[0250] Referring now to FIG. 9 , a spontaneous architecture can be implemented to support applications and services that undergo highly dynamic changes in metric (e.g., time, data, conditions, demand, coordinates, actions, relative position, and events) states. For example, an autonomous vehicle management system may manage an ecosystem of autonomous vehicles that move across a grid of control where the grid of control is dynamically controlled based on traffic conditions on the roads. In embodiments, this grid topology may be dynamically reconfigured to enable support for highly dynamic changes in environment and conditions. In further examples, a spontaneous architecture may provide an air traffic control system or a military theater or drone swarm where cohorts are constantly changing and may have highly dynamic security responses to the environment. In embodiments, the spontaneous topology can change by modifying DNA in response to situational events. To accommodate these situational events, the modified DNA can be dynamically distributed to different cohorts or groups.

[0251] In embodiments, such architectures—spontaneous architectures—can benefit from complete and / or real-time reconfiguration of their network topology to address specific control parameters, such as metric states or operator preferences. In certain situations, network architectures are required to address additional challenges in that they are unable to support highly dynamic changes in metrics and the diversity of their possible states. In embodiments, spontaneous architectures address these highly dynamic changes in metrics that enable support for emerging ultra-bandwidth applications or artificial intelligence portals. Further applications of spontaneous architectures include military theaters, power grid management, and highly distributed financial trading systems.

[0252] In embodiments, the Ecosystem VDAX can build and control a genome network topology that supports applications requiring dynamic state attributes. In embodiments, the Ecosystem VDAX may be able to control engagement of cohorts within the ecosystem using different genome constructs (e.g., LNAs and XNAs), such that engagement between cohorts within the ecosystem (e.g., via link exchange) can be enabled, prevented, and revoked by the community owner by controlling the LNAs and / or XNAs provided to different members. Similarly, in these examples, the Ecosystem VDAX can change the dynamic network topology of the ecosystem by controlling the LNAs and XNAs provided to different cohorts.

[0253] In embodiments, enclave VDAXs can be configured to control their respective portions of the ecosystem's genome network topology, whereby the enclave VDAXs are responsible for specific VDAX-specified functions and processes related to their portions of the genome network topology. In further embodiments, genome correlation and differentiation allows enclave VDAXs to configure dynamic genome network topologies without having to modify the physical topology. For example, in these embodiments, enclave VDAXs can control the engagement of cohorts in different enclaves using different genome constructs (e.g., LNAs and XNAs), such that engagement (e.g., link exchange) can be dynamically enabled, prevented, or reinstated by the enclave VDAXs by controlling the LNAs and / or XNAs provided to members of different enclaves. Similarly, in these examples, enclave VDAXs can create new dynamic spontaneous enclaves by controlling the genome constructs (e.g., LNAs and XNAs) provided to different cohorts. In some embodiments, a cohort VDAX can control its respective portion of the ecosystem genome network topology. In these embodiments, the cohort VDAX can be responsible for performing specific functions for its respective portion of the genome network topology specified by the ecosystem VDAX and / or enclave VDAX. In further embodiments, a cohort VDAX performing such functions can enable a proactive architecture by configuring a dynamic genome network topology without the need to modify the physical network topology.For example, in these embodiments, cohort VDAXs can use different genomic structures (e.g., LNAs and XNAs) to control engagement of VDAXs to designated portions of the genome network topology, such that engagement between VDAXs (e.g., through link exchange) in designated portions can be dynamically enabled, prevented, and / or revoked by designated cohort VDAXs by selectively modifying the LNAs and / or XNAs of those VDAXs.

[0254] In embodiments, various types of interactions (e.g., ecosystem VDAX to enclave VDAX, enclave VDAX to cohort VDAX, ecosystem VDAX to cohort VDAX, and / or cohort VDAX to cohort VDAX interactions) may be controlled by specific genomic structures (e.g., CNAs, PNAs, LNAs, and XNAs) determined by the ecosystem VDAX. For example, in these embodiments, the ecosystem VDAX may control engagement between VDAXs corresponding to respective portions of the genome network topology by dynamically altering some or all of the genomic structures (e.g., LNAs or XNAs) of the ecosystem, enclave, and / or cohort VDAXs in their respective portions of the genome network topology. In this manner, VDAXs may be added, prevented, and / or revoked from different portions of the genome network topology via their respective genomic structures. For example, in some embodiments, enclave VDAXs designated by controlling respective portions of the genome network topology can modify those portions by selectively altering the genomic structures of VDAXs to be added and / or withdrawn from their respective portions of the genome network topology. In embodiments, genomic constructs (e.g., CNAs, PNAs, LNAs, and / or XNAs) responsible for ecosystem, enclave, and cohort engagement can be modified to alter the basis for differentiation and / or correlation, thereby modifying the genome network topology. In some embodiments, such modifications can be performed as part of a genome network topology update (e.g., cohort withdrawal). In embodiments, these genomic structures can be modified to allow for controlling cohort engagement in an ecosystem with a different genome topology. Constructs (e.g., LNAs and XNAs) can enable, prevent, or withdraw engagement between members in an ecosystem (e.g., via link exchange) by controlling the genomic constructs.Similarly, in these examples, ecosystem CNAs, PNAs, LNAs and / or XNAs can alter the dynamic network topology of the ecosystem by controlling the genomic structures presented to different cohorts.

[0255] In embodiments, spontaneous architectures preserve their operational integrity regardless of the dynamic frequency of the metric states (e.g., time, data, conditions, requests, coordinates, actions, or events) they support. For example, in some embodiments, cohorts may operate in environments where the reporting frequency of metrics may be variable. In these embodiments, the spontaneous architecture handles these fluctuations in overall metric data while maintaining the overall integrity of the ecosystem.

[0256] FIG. 9 illustrates an example of a security platform with a spontaneous architecture that provides services to a dynamic ecosystem 900. In an embodiment, the dynamic ecosystem 900 includes an enclave and its cohorts supporting applications and services that undergo highly dynamic changes in state (time, data, conditions, demand, coordinates, behavior, etc.). Examples of dynamic ecosystems 900 include artificial intelligence applications, autonomous vehicle systems, and real-time supply chain systems. Spontaneous ecosystems 900 often require complete reconfiguration in real time in response to specific conditions and / or operator preference(s). The security requirements of these ecosystems are such that traditional cryptographic protocols cannot support dynamic frequencies and are incompatible with the various states that may be present. In an embodiment, the security platform is implemented as a spontaneous architecture to provide the spontaneous ecosystem 900. These architectures hold great promise for the emerging integration of ultra-bandwidth and artificial intelligence (AI) portals.

[0257] In a spontaneous architecture, ecosystem VDAX 902 may be configured to dynamically define enclaves 904 and / or assign cohorts 906 to one or more enclaves 904 in real time. In some of these embodiments, an AI portal may be utilized by VDAX 902 to define enclaves 904 and assign cohorts 906 thereto. For each cohort 906, VDAX 902 may initially generate and provide genomic information for the cohort 906. This genomic information may be generated and provided daily, whenever the cohort 906 is powered on, or at other suitable intervals. The genomic information may be correlated with all other cohorts 906 in ecosystem 900, but is not assigned to a specific enclave 904. When a cohort 906 joins the ecosystem 900, VDAX 902 and the AI ​​portal 910 may determine which enclaves 904 the cohort 906 belongs to and which enclaves 904 the cohort 906 should be revoked from. For each enclave 904 to which a cohort 906 belongs, VDAX 902 may propagate modifications to the cohort's XNAs and LNAs so that the AI ​​portal 910 can decrypt VBLSs generated by the cohort 906 within those enclaves 904. Similarly, for each enclave 904 from which a cohort 906 has been revoked, VDAX may propagate changes to the cohort's XNAs and LNAs so that the cohort 906 can no longer decrypt VBLSs generated for the remaining cohorts 906 within those enclaves 904. In a voluntary ecosystem, a VDAX (or multiple VDAXs) may manage membership for enclaves 904 within ecosystem 900 in this manner, such that cohorts 906 within grid 912 maintain a high level of correlation with other cohorts 906 within enclave 904, and such that cohorts 906 that are no longer within grid 912 no longer maintain a high level of correlation.

[0258] In the illustrated example, the spontaneous ecosystem 900 is an autonomous vehicular environment. In such an environment, vehicles may traverse roadways in a region (e.g., an entire city, an entire state, etc.). At times, there may be hundreds of thousands of vehicles traversing the roadways, and at other times, there may be fewer vehicles present. Each vehicle may be configured to report its sensor data (e.g., LIDAR, radar, video, moisture, etc.) to a cloud-based system, which may maintain status data about the roadways (e.g., the location of vehicles, obstacles, traffic, etc.). The cloud-based system may be configured to report status data relevant to each vehicle to inform the vehicles of conditions along their path (or other appropriate data, such as instructions for a particular vehicle). Because each vehicle travels along its own unique route, the amount of data collected per second from a fleet of vehicles can be enormous. In the illustrated example, VDAX 902 and the AI ​​portal use VBLS to facilitate reporting of relevant status data to vehicles along the grid. In this example, VDAX 902 may generate a grid corresponding to an area (e.g., a city, a county, a state, etc.), where grid 912 has cells. The cells may be fixed in size or may be dynamically sized depending on the amount of traffic on the road. Similarly, cells may be fixed in number or dynamically allocated depending on the amount of traffic on the road. In some embodiments, the AI ​​portal determines the number of cells and / or the size of the cells depending on the road conditions (e.g., how many vehicles are on the road, how many vehicles have traditionally been on the road at this time, etc.). In embodiments, each cell is considered an enclave 904, and the cloud-based system may report status data related to those vehicles within enclave 904. In some embodiments, a communication tower (e.g., a 5G tower) may host an enclave VDAX that communicates with cohort 906 within enclave 904. As a vehicle crosses a roadway, the vehicle may exit one cell and enter another.Additionally, because a vehicle is likely to travel straight, turn right, or turn left, VDAX902 may assign the vehicle to multiple cells (i.e., enclaves) so that the vehicle can receive relevant state data for one or more cells immediately preceding the vehicle, one or more cells to the right of the vehicle, and one or more cells to the left of the vehicle. VDAX902 may provide the vehicle with genomic information for each of these enclaves (e.g., one or more cells to the right, left, and ahead of the vehicle). For each cell / enclave, the vehicle (e.g., a cohort VDAX running on it) may generate a GEC and exchange the GEC with the cloud-based system to authenticate itself for a particular cell. Once authenticated, the vehicle and the cloud-based system may exchange links for engagement for each cell. As the vehicle collects sensor data, the vehicle can generate a VBLS based on the collected sensor data, the XNA, and information contained in the link received from the cloud-based system. Similarly, for each cell, the cloud-based system may broadcast a VBLS generated in a VBLS specific to that cell (e.g., understood by any cohort assigned to the enclave). When a vehicle leaves a cell, VDAX may modify the vehicle's genetic information corresponding to that cell so that the vehicle can no longer understand or generate the VBLS corresponding to that cell.

[0259] The application ecosystem continues to evolve, featuring complex services and processes that require greater resource availability and lower network latency. This is evidenced by process redistribution and infrastructure relocation. Applications typically require sophisticated operating systems. OS services are increasingly bifurcated, with those with lower complexity and resource requirements hosted locally (e.g., client OS) and those with higher complexity and resource requirements hosted remotely (e.g., cloud OS). This efficient OS bifurcations also have significant benefits, including widespread adoption of very low-cost client devices with access to powerful non-resident capabilities, very low bandwidth budgets, and the free distribution and development of powerful new applications. These apparently new and beneficial applications, like their predecessors, will pose significantly more complex challenges to access and proprietary controls.

[0260] Referring now to FIG. 10, by implementing a computationally complex genome structure, CG-ESP enables a method for uniquely transforming the interactions between different software and hardware components, such as applications (APIs, libraries, and threads), operating systems (kernels, services, drivers, and libraries), and systems-on-chip (processing units, e.g., cores). These ecosystem components can execute executable binaries collaboratively or independently. In embodiments, the method may enable such ecosystems and enclaves, and cohorts (independent and dependent)—best attended to their capabilities, limitations, and performance efficiencies—to be specifically specified and organized to form a normatively constructed transient architecture. In embodiments, the transient architecture is capable of transforming executable binaries into VBLS digital objects and resulting VBLS streams that exhibit unique genome differentiation and correlations. In some of these embodiments, CG-ESP in the transient architecture is capable of computationally complex genome construction that facilitates engagement with other CG-enabled architectures, such as:

[0261] (Directed states (static ecosystems and free-form ecosystems) and spontaneous states (dynamic states)) In embodiments, transient architectures may offer many advantages in that many of their attributes show direct correlations with other architectures (e.g., directed and spontaneous). However, transient architectures constitute a very different attack surface in that their components are typically tightly coupled and their processes are highly observable and modifiable before and during the process. Therefore, under these hypothetical conditions, the dynamic virtual trusted execution domains facilitated by VBLS may be useful.

[0262] In embodiments, genomic correlation and differentiation enable a temporal architecture that can be genomically configured by VDAX to enable a viable ecosystem (e.g., Ecosystem VDAX, Enclave VDAX, Cohort VDAX, and Dependent VDAX). In further embodiments, the temporal architecture bases a viable ecosystem in which VDAXs at different hierarchical levels provide deep knowledge of sources that enable the establishment of trusted components. In further embodiments, a complex ecosystem, such as an autonomous vehicle, or spacecraft, or mobile phone, or web services architecture, may consist of numerous components, each of which is composed of further subcomponents, and in this example, each tier of the ecosystem allows the construction of a system of component source knowledge through its respective VDAX and genomic configuration. In further embodiments, each component can perform operations to verify the provenance and authenticity of the subcomponents. In this example, the authenticity of the operations may be underwritten by genomically configurable exchange with a trusted provisioning source.

[0263] In embodiments, a genomically constructed application-specific executable ecosystem does not require modification of its underlying architectural embodiment. In further embodiments, the underlying architecture remains unchanged, and the executable ecosystem exists as an information overlay that can provide source knowledge. In further embodiments, source knowledge of components of the transient architecture can be applied to validate the operational parameters of the executable ecosystem.

[0264] In embodiments, an executable ecosystem VDAX can independently initiate a unique genome correlation construct. In further embodiments, the correlation construct may provide verification of the attribution of subcomponents within the enclave VDAX. In some embodiments, the correlation construct may include LNA (Genome Correlation), CNA (Genome Engagement-Completeness), and / or PNA (Genome Engagement-Quality). In this example, these constructs may enable virtual authentication.

[0265] In embodiments, an executable ecosystem VDAX can independently initiate its own genome differentiation construct (e.g., ZNA). In some of these embodiments, executable ecosystem-driven differentiation constructs may be applied to determine which components are responsible for specific operations within the ecosystem and / or determine which components are isolated. In some embodiments, differentiation constructs may include ZNA (genome code isolation). In this example, these differentiation constructs may enable virtual affiliation.

[0266] In embodiments, a viable ecosystem VDAX may directly acquire its inherent genome correlation structure. In further embodiments, the correlation construct may provide validation of subcomponent attribution within a genome progeny VDAX, such as an enclave VDAX or a cohort VDAX. In some embodiments, the correlation construct may include LNA (Genome Correlation), CNA (Genome Engagement-Complete), and / or PNA (Genome Engagement-Quality). In this example, these constructs may enable virtual authentication.

[0267] In embodiments, viable enclave VDAX may directly acquire their unique genomic differentiation constructs. In further embodiments, viable enclave-driven differentiation constructs may be applied to determine which components are responsible for a particular operation, or which components are sole. In some embodiments, differentiation constructs may include ZNAs (genomic code isolation). In this example, these differentiation constructs may enable virtual affiliation.

[0268] In embodiments, an executable enclave VDAX can directly obtain its own genome correlation syntax. In further embodiments, the correlation construct may provide validation of the attribution of subcomponents within a genome bequest VDAX, such as a subordinate VDAX or a cohort VDAX. In some embodiments, the correlation construct may include LNA (Genome Correlation), CNA (Genome Engagement-Complete), and / or PNA (Genome Engagement-Quality). In this example, these constructs may enable virtual authentication.

[0269] In embodiments, an executable cohort VDAX can directly acquire its own genomic correlation syntax. In further embodiments, the correlation constructs may provide validation of the attribution of subcomponents within a genomic corpus VDAX, such as a subordinate VDAX. In some embodiments, the correlation constructs may include LNA (Genomic Correlation), CNA (Genomic Engagement-Completeness), and / or PNA (Genomic Engagement-Quality). In this example, these constructs may enable virtual authentication.

[0270] In embodiments, viable cohort VDAX may directly acquire their unique genomic differentiation construction. In further embodiments, viable enclave-initiated differentiation construction may be applied to determine which components are responsible for particular operations or the determination that a component is isolated. In some of these embodiments, viable ecosystem-initiated differentiation construction may be applied to determine which components are responsible for particular operations and / or the determination that a component is isolated within the ecosystem. In some embodiments, differentiation construction may include ZNA (genomic code isolation). In this example, these differentiation constructions may enable virtual affiliation.

[0271] In embodiments, executable VDAXs (with which unrelated VDAXs engage) may acquire their unique genome correlation configurations through alternative genome subconfigurations. In further embodiments, correlation constructs may provide validation of the attribution of unrelated components. In some embodiments, correlation constructs may include LNA (Genome Correlation), CNA (Genome Engagement-Complete), and / or PNA (Genome Engagement-Quality). In this example, these configurations may enable virtual authentication.

[0272] In embodiments, executable VDAXs (with which unrelated VDAXs engage) may acquire their unique genome differentiation constructions through alternative genome subassemblies. In further embodiments, executable enclave-initiated differentiation constructions may be applied to determine which components are responsible for specific operations or to determine that components are isolated. In some of these embodiments, executable ecosystem-driven differentiation constructions may be applied to determine which components are responsible for specific operations within the ecosystem and / or to determine that components are isolated. In some embodiments, differentiation constructions may include ZNA (genome code isolation). In this example, these differentiation constructions may enable virtual affiliation.

[0273] In embodiments, multiple constructed executable genomic topologies may exist simultaneously. In some embodiments, these multiple genomic topologies may provide genomically valid operation for different architectural functions. In these exemplary embodiments, the different architectural functions may include source verification, operational verification, or license fee payment verification.

[0274] In embodiments, each viable ecosystem has a unique genome architecture from which correlation and differentiation attributes are derived, and these attributes control the topology of the genome of that enclave. In some embodiments, each viable enclave has a unique genome architecture from which correlation and differentiation attributes are derived, and these genome attributes can thereby control the topology of the genome of its cohort. In further embodiments, these unique architectures provide differentiation across species, offspring, and siblings.

[0275] In embodiments, a transient architecture with various genomically engineered configurations can transform binary data as VBLS-based digital objects and / or streams. In embodiments, the transient architecture security platform provides virtual agility. In embodiments, VBLS may refer to a language spoken by an enclave or cohort to another enclave or cohort, uniquely understood by the recipient enclave or cohort, i.e., a language only the recipient enclave or cohort can understand. In this example, an intruder targeting a rogue cohort, virus, malware, etc., cannot generate or decipher VBLS between legitimate cohorts.

[0276] In embodiments, VBLS-transformed binary data may be exchanged and executed by components from two or more different configurations that share common genomic correlations and differentiations. In embodiments, the different configurations provide state information for operational parameters of a viable ecosystem. In further embodiments, these different configurations are each capable of operating according to their state and functional components with knowledge of the associated configuration.

[0277] In embodiments, the transient architecture may have multiple genomically constructed configurations, and certain components may be capable of converting executable binaries into Virtual Binary Language Script (VBLS)-based digital objects and / or streams. In further embodiments, executable binaries may be converted into VBLS digital objects or VBLS streams, with this conversion being achieved by application of genomically constructed configuration components. In further embodiments, the conversion is achieved by applying genomic sequence mapping and conversion. In embodiments, sequences are central to computationally converting digital objects into unique, non-recurrent genomic engagement elements. In examples, sequences may be widely heterogeneous, and sequences may require processing that results in a particular level of entropy. In embodiments, sequence mapping may be compatible with a wide range of protocols and formats, or may start with objects exhibiting existing entropy, and these objects may be converted into objects exhibiting a particular level of entropy through computationally complex genomic processing and functions.

[0278] In embodiments, a VBLS-transformed executable binary can be swapped and executed by components from two or more different architectures that have common genomic correlations and differentiations.

[0279] In embodiments, within a particular transient architecture, a component may transform a VBLS executable binary (e.g., a proprietary computer application) such that the transformed executable binary can only be correctly processed by a particular hardware component (e.g., an SoC Core), which shares common genomic correlations and differentiation. In further embodiments, the particular hardware is part of a genomic ecosystem and can apply genomic correlation processing to enable processing of the VBLS executable binary.

[0280] In embodiments, a transient architecture VDAX resident component may convert a (VBLS) executable binary (e.g., a proprietary computer application) such that the converted executable binary can only be correctly processed by another transient architecture VDAX-specific hardware component (e.g., an SoC core), with which the components share common genomic correlations and differentiation. In further embodiments, another transient architecture VDAX may be part of a genomic ecosystem.

[0281] In embodiments, within a particular transient architecture, two or more components can transform executable binaries (e.g., proprietary computer applications) based on unique genomic structures known to another component. These transformed binaries can be reshaped as executable binaries only by one of these components, and not by the other. The transformation and execution of executable binaries occurs in-place. In embodiments, the components are part of the same genomic ecosystem or genomic enclave.

[0282] In embodiments, within a particular temporary architecture, a particular component may transform an executable binary (e.g., a proprietary computer application) based on a unique genome structure, which is known to particular components of other temporary architectures. A transformed binary from one architecture can be re-formed as a binary that is executable only by particular components of other architectures. The reformation and execution of the executable binary occurs in-place. In further embodiments, temporary architectures that share a component are part of the same genome ecosystem or genome enclave. In further embodiments, components sharing a transformed executable binary engage in genome link exchange to provide knowledge of the source of the component. In further embodiments, knowledge of the source of the component is used together with further genome construction to establish trust relationships between the components.

[0283] In embodiments, within a particular temporary architecture, a component VDAX may transform an executable binary (e.g., a proprietary computer application) based on a specific, unique genomic structure, known only to that component. Such a transformed binary can be re-formed as an executable binary only by that particular component, and not by other components. The modification and execution of the executable binary by that particular component occurs in-place. In embodiments, these component-specific transformations apply a genomic configuration based on genomic data known only to the associated component VDAX. In further embodiments, the transformed component is capable of operating in a secure manner such that any modifications applied to the transformed binary by anyone other than the component render the transformed executable binary inoperable.

[0284] FIG. 10 illustrates an example of a security platform with a transient architecture that provides services to an executable ecosystem 1000. As described, the executable ecosystem 100 may be any self-contained ecosystem, such as a computing device (e.g., a server, a mobile device, a personal computer, a laptop computer, etc.). In an embodiment, the transient architecture provides a framework for cohorts 1006 to create and decode VBLS-based isolation of executable code instances, thereby providing real-time virtual trusted execution domains that are not subject to intelligent external observation. For example, in a computing device, enclaves 1004 may include the computing device's system-on-chip (SoC), the device's operating system, and applications. In this example, the ecosystem, the independent cohorts of system-on-chip enclaves 1006 may include processor cores, memory devices (e.g., RAM, ROM), etc. The independent cohorts of operating system enclaves 1006 may include the operating system kernel, various drivers (network drivers, file system drivers, print drivers, video drivers, camera drivers, subordinate "delegates" cohorts, etc.), shared libraries, etc. In examples, the dependent cohorts 1008 of an application may include threads, APIs, files, etc. In some embodiments, each enclave (SoC, operating system, application) is assigned a genomic dataset (e.g., ZNA, LNA, CNA, and / or PNA), which may be inherited by each enclave's cohorts. In embodiments, ecosystem VDAX may create transient enclaves when an application is accessed, whereby transient enclaves are created for the application's cohort, the cohort of operating systems engaged by the application, and the cohort of SoCs invoked by the operating systems when running the application.In this example, cohorts within a transient enclave can authenticate with each other, exchange links, and generate VBLSs. During execution, a particular thread in an application may request resources from the operating system kernel. As a thread executes, an independent cohort VDAX representing the application thread generates a VBLS based on the thread's executable code requesting the kernel's resources. In this scenario, the application thread may be authenticated by an independent cohort VDAX representing the kernel (e.g., kernel VDAX) using ecosystem genome progeny data generated using the ecosystem CNAs assigned to the thread application (or vice versa). In response, the kernel VDAX and the thread VDAX representing the thread exchange links generated using the kernel's and application thread's respective LNAs. The thread VDAX can generate a VBLS based on the executable code instance requesting the resources, the ZNAs assigned to the application thread, and the links provided by the kernel VDAX. The thread VDAX provides the VBLS to the kernel VDAX, which then decrypts the VBLS. The kernel VDAX may interface with the VDAX corresponding to the requested resource (e.g., a camera driver accessing a computing device's camera, a subordinate "surrogate" cohort) using a VBLS that is only decipherable by the kernel or the requested resource.

[0285] In some embodiments, dependent applications (as opposed to independent applications) are not capable of secure VBLS isolation of their internal API and thread components. However, both independent and dependent applications are capable of secure VBLS inter-process communication with each other and with authorized external resources (e.g., operating systems, systems on chips). In these embodiments, transient enclaves enable secure VBLS isolation of the kernel and processing cores, ensuring that all digital objects on the system bus are engaged only by a specific application, operating system, and / or SoC cohort.

[0286] 11 illustrates an example set of process CG-based operations performed by a set of members in a CG-enabled ecosystem to facilitate VBLS-based data exchange. The processes, modules, and techniques described with respect to FIG. 11 are provided as example implementations of CG-based operations that may be performed by a VDAX executing a particular configuration of CG-ESP and are not intended to limit the scope of the present disclosure. It will be understood that different CG-ESPs may be configured to perform different CG-based operations and may accordingly implement VBLS-based data exchange.

[0287] In an example, an ancestor VDAX 1102 (e.g., an ecosystem VDAX or an enclave VDAX) may be configured to add X community members (e.g., independent cohorts) to a community (e.g., an enclave) by digitally generating respective genomic datasets (DNA) for a set of descendant VDAXs 1104-1, 1104-2, .... Each of the X community members 1104-X includes a first descendant VDAX 1104-1 and a second descendant VDAX 1104-2. In an embodiment, the ancestor VDAX 1102 and the descendant VDAXs 1104-1, 1104-2, .... 1104-X execute respective CG-ESP instances, including a root DNA module, a link module, a sequence mapping module, and a binary conversion module (e.g., as described with respect to FIG. 4). It will be understood that the CG-ESP instances may include additional and / or alternative modules without departing from the scope of this disclosure.

[0288] In some embodiments, the root DNA module of the ancestral VDAX1102 may digitally generate the ancestral genome dataset or may be assigned the ancestral genome dataset from another VDAX. In embodiments, the root DNA module of the ancestral VDAX1102 digitally generates a respective descendant genome dataset for each descendant VDAX1104 in the digital community (e.g., ecosystem or enclave) and assigns each descendant genome dataset to each descendant VDAX1104. In some of these embodiments, the ancestral VDAX1102 may digitally generate each descendant genome dataset from the ancestral genome dataset assigned to the ancestral VDAX1102 using a set of information-theoretically facilitated computational complexity functions.

[0289] In embodiments, the genomic dataset of ancestral VDAX 1102 may include ancestral genome eligibility objects (e.g., CNAs and / or PNAs), ancestral genome correlation objects (e.g., LNAs), and / or ancestral genome differentiation objects (e.g., XNAs or ZNAs). In some of these embodiments, ancestral VDAX 1102 may assign highly correlated (e.g., identical or otherwise sufficiently correlated) genomic correlation and differentiation objects to each of descendant VDAX 1104, assuming all of the descendant VDAXs 1104 are allowed to communicate (e.g., the descendant VDAXs share a mutual identity of interest). In some embodiments, ancestral VDAX 1102 may use a set of information-theoretically driven, computationally complex functions to generate unique but highly correlated genomic eligibility objects.

[0290] In an embodiment, the genomic dataset of ancestral VDAX1102 is provided to ancestral VDAX1102 in a one-time, "trusted" event. For example, the descendant genomic data may be provided to the device running ancestral VDAX1102 on a USB stick or other connectable physical medium, via wired communication between ancestral VDAX1102 and descendant VDAX1104 (e.g., a physical digital communication port on the device running ancestral VDAX1102), via a proximity-based wireless protocol (e.g., near-field communication), via a VBLS that is generated and decoded using a different genomic dataset when the device hosting descendant VDAX1104 is initially manufactured or configured, and / or the like. Once descendant VDAX1104 has been provided with its descendant genomic dataset for a particular community, descendant VDAX1104 can engage with any other des...

Claims

1. An ecosystem security platform executed by a VDAX processing system, the ecosystem security platform comprising: a root DNA module that manages a digitally generated genomic dataset assigned to the VDAX, the genomic dataset being specific to the VDAX and including a genomic eligibility object, a genomic correlation object, and a genomic differentiation object, and that is configured to modify the genomic dataset using one or more computationally complex functions; receiving a link from a second VDAX, the link including encoded genome regulatory instructions; a link module configured to decode the link based on the genome eligibility object and the modified genome correlation object to obtain a decoded genome regulatory instruction, wherein the modified genome correlation object is modified from the genome correlation object by the root DNA module; and a sequence mapping module configured to obtain a sequence from a digital object to be provided to the second VDAX, the sequence being extracted from a first portion of the digital object, and to map the sequence to a modified genome differentiation object to obtain a genome engagement factor, such that the modified genome differentiation object is modified from the genome differentiation object by the root DNA module based on the decoded genome regulatory instructions; and a binary conversion module configured to encode a second portion of the digital object based on the genome engagement factor to obtain an encoded digital object, the binary conversion module generating a Virtual Binary Language Script (VBLS) object comprising the encoded digital object and the first portion of the digital object; The VBLS object is provided to the second VDAX as part of a set of VBLS objects, an ecosystem security platform.

2. 10. The ecosystem security platform of claim 1, wherein the digital object is part of a set of digital objects that are each encoded into a set of respective VBLS objects.

3. 3. The ecosystem security platform of claim 2, wherein each digital object is encoded with a different respective genome engagement factor.

4. 4. The ecosystem security platform of claim 3, wherein the sequence mapping module obtains a respective sequence from the respective digital object and maps the respective sequence to a modified genome differentiation object to obtain a respective genome engagement factor used to encode the respective digital object.

5. The ecosystem security platform of claim 1 , wherein the set of VBLS objects is a non-recursive language specific to the VDAX and the second VDAX.

6. The ecosystem security platform of claim 1 , wherein the sequence is a public sequence defined in the first portion of the digital object according to a known protocol and format.

7. 2. The ecosystem security platform of claim 1, wherein the sequence is a private sequence defined in the first portion of the digital object according to a proprietary protocol and format that is not publicly available.

8. 2. The ecosystem security platform of claim 1, wherein the binary conversion module includes a disambiguation module that encodes the second portion of the digital object based on the genome engagement factor by determining a disjunctive sum of the second portion and the genome engagement factor using an XOR operation.

9. 2. The ecosystem security platform of claim 1, wherein the binary conversion module includes an encryption module that encrypts a second portion of the digital object based on the genome engagement factor by encrypting the second portion using a cryptographic function and the genome engagement factor.

10. 10. The ecosystem security platform of claim 1, wherein the link module decodes the link from the second VDAX as part of a link exchange process with the second VDAX.

11. 11. The ecosystem security platform of claim 10, wherein the link exchange process is a one-time process.

12. 12. The ecosystem security platform of claim 11, wherein the link exchange step includes authenticating an ecosystem member associated with the second VDAX based on a genomic eligibility object of the VDAX.

13. 11. The ecosystem security platform of claim 10, wherein the link module is further configured to produce a second link including the second encoded genome regulatory instructions, the second link being provided to the second VDAX as part of the link exchange process.

14. 14. The ecosystem security platform of claim 13, wherein the link exchange process is a doubly symmetric process such that the second link is produced independently of the link received from the second VDAX.

15. The link module, in generating the second link, further comprises: Determine the second genome control command, encoding a second genome regulatory instruction using a link genome engagement factor to obtain a second encoded genome regulatory instruction, wherein the link genome engagement factor is determined by the sequence mapping module based on the link mapping sequence and the second modified genome correlation object modified from the genome correlation object by the root DNA module; and generating a genome engagement cargo comprising a link mapping sequence, a second encoded genome regulatory instruction, and encoded link decoding information, wherein the link mapping sequence is not obfuscated in the genome engagement cargo, and the second link comprises the genome engagement cargo; and The ecosystem security platform of claim 13, configured to provide a second link to a second VDAX, and the second VDAX decodes the second encoded genome control instructions based on the link mapping sequence, the encoded link decoding information, and the second genome dataset assigned to the second VDAX.

16. 16. The ecosystem security platform of claim 15, wherein the link mapping sequence remains unencoded in the genome engagement cargo.

17. The binary conversion module further: receiving a second VBLS object from a second VDAX, the second VBLS object including a second encoded digital object and unencoded metadata; and decoding the second digital object based on a second genome engagement factor to obtain an unencoded second digital object, the second genome engagement factor being determined by the sequence mapping module based on a second sequence and a second modified genome differentiation object derived by the root DNA module from a genome differentiation object of the genome dataset based on a second genome regulatory instruction; 16. The ecosystem security platform of claim 15, configured to:

18. The ecosystem security platform of claim 1 , wherein the link module is configured to perform a link exchange process across a set of interoperable digital communication media.

19. 10. The ecosystem security platform of claim 1, wherein the link module is configured to perform a link exchange process across a set of interoperable digital networks.

20. 10. The ecosystem security platform of claim 1, wherein the link module is configured to perform a link exchange process across a set of interoperable digital devices.

21. 10. The ecosystem security platform of claim 1, wherein the link module is configured to perform a link exchange that is performed asynchronously with respect to the second VDAX.

22. 2. The ecosystem security platform of claim 1, wherein the link module is configured to perform a link exchange process in a symmetric manner such that the VDAX does not provide a second link to the second VDAX.

23. 2. The ecosystem security platform of claim 1, wherein the link module is further configured to verify an eligibility correlation for the second VDAX based on a genomic eligibility correlation object of the VDAX.

24. 2. The ecosystem security platform of claim 1, wherein the link module is further configured to verify a link exchange correlation for the second VDAX based on the link received from the second VDAX and a genome correlation object of the VDAX.

25. The ecosystem security platform of claim 1 , wherein the link module is configured to facilitate secure exchange of link information using a series of computationally complex functions.

26. 26. The ecosystem security platform of claim 25, wherein the set of computationally complex functions is one of cryptography-based functions, non-cryptography functions, or hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

27. 2. The ecosystem security platform of claim 1, wherein the link module is configured to execute a series of processes to securely exchange link information with the second VDAX to enable asymmetric engagement, wherein the exchange of link information exhibits the same level of entropy as the asymmetric engagement.

28. 10. The ecosystem security platform of claim 1, wherein the link module comprises a static link module configured to generate and decode static links.

29. 30. The ecosystem security platform of claim 28, wherein the static link module generates the static link according to rules and processes prescribed by a best-in-class ecosystem VDAX within the digital ecosystem.

30. 2. The ecosystem security platform of claim 1, wherein the link module includes a dynamic link module configured to generate and decode dynamic links, and the link received from the second VDAX is a dynamic link further including an instruction set that, when executed by the VDAX, preferentially applies to the respective configuration of at least one of a root DNA module, a sequence mapping module, or a binary conversion module such that the modified configuration is executed only upon generation of a VBLS to be provided to the second VDAX.

31. 10. The ecosystem security platform of claim 1, wherein the sequence mapping module is configured to select a public sequence from each first portion of each digital object formatted according to a published protocol.

32. The sequence mapping module maps sequences to modified genome differentiation objects. Process the array to derive intermediate values, The ecosystem security platform of claim 1, configured to generate a genome engagement factor based on the intermediate value and the modified genome differentiation object using a set of information-theoretically facilitated computational complexity functions.

33. 33. The ecosystem security platform of claim 32, wherein the set of information-theoretically accelerated computationally complex functions is either cryptography-based functions, non-cryptography functions, or hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

34. The ecosystem security platform of claim 1 , wherein the genome engagement factor is a binary vector exhibiting a particular entropy.

35. 35. The ecosystem security platform of claim 34, wherein the specific entropy of the genomic engagement factor is greater than or equal to the specific entropy of the sequence.

36. 10. The ecosystem security platform of claim 1, wherein the sequence mapping module is configured to select a sequence from each first portion of each digital object formatted according to a proprietary protocol.

37. The ecosystem security platform of claim 1 , wherein the root DNA module includes a CNA module that forms and constructs genome-qualified objects.

38. 38. The ecosystem security platform of claim 37, wherein the CNA module is configured to employ information theory-facilitated genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems.

39. 38. The ecosystem security platform of claim 37, wherein the link module uses the genome eligibility object to verify genome engagement integrity with the second VDAX.

40. 38. The ecosystem security platform of claim 37, wherein the genomic eligibility object is a CNA object that exhibits a particular entropy, and the CNA object enables genomic processes based on differences and correlations.

41. 41. The ecosystem security platform of claim 40, wherein the CNA object is an N-dimensional binary vector that indicates the particular entropy.

42. 41. The ecosystem security platform of claim 40, wherein the specific entropy of a CNA object is configurable by a community owner.

43. 41. The ecosystem security platform of claim 40, wherein the CNA module is configured to generate respective sets of CNA objects based on the PNA objects of the VDAXs, each set of CNA objects being assigned to a respective set of descendant VDAXs, each CNA object exhibiting a particular entropy equal to the entropy of the CNA objects of the VDAXs.

44. 41. The ecosystem security platform of claim 40, wherein the CNA module is configured to use a set of information-theoretically accelerated computational complexity functions to perform eligibility correlations with respect to other VDAXs based on engagement information provided by the CNA object and the other VDAXs.

45. 45. The ecosystem security platform of claim 44, wherein the set of information-theoretically accelerated computationally complex functions is either cryptography-based functions, non-cryptography functions, or hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

46. 41. The ecosystem security platform of claim 40, wherein the CNA module is configured to establish specific relationships between other VDAXs in a digital ecosystem to which the VDAX belongs based in part on the VDAX's CNA object.

47. The ecosystem security platform of claim 1 , wherein the root DNA module includes a PNA module that forms and constructs genome-qualified objects.

48. 48. The ecosystem security platform of claim 47, wherein the PNA module is configured to employ information theory-facilitated genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems.

49. 48. The ecosystem security platform of claim 47, wherein the link module uses the genomic eligibility object to confirm genomic engagement eligibility with the second VDAX.

50. 48. The ecosystem security platform of claim 47, wherein the genomic eligibility object is a PNA object that exhibits a particular entropy, and the PNA object enables genomic processes based on differences and correlations.

51. 51. The ecosystem security platform of claim 50, wherein the PNA object includes a first N-dimensional binary vector and a second N-dimensional binary vector indicating the particular entropy, the first N-dimensional vector consisting of M randomly chosen binary primitive polynomials of degree T such that M×T equals N, and the second N-dimensional vector is determined based on the first N-dimensional binary vector.

52. 51. The ecosystem security platform of claim 50, wherein the specific entropy of a PNA object is configurable by a community owner.

53. 51. The ecosystem security platform of claim 50, wherein the PNA module is configured to generate respective sets of PNA objects based on the PNA objects of the VDAXs, each set of PNA objects being assigned to a respective set of descendant VDAXs, each PNA object exhibiting a particular entropy equal to the entropy of the PNA objects of the VDAXs.

54. 51. The ecosystem security platform of claim 50, wherein the PNA module is configured to use a set of computational complexity functions based on information theory to perform eligibility correlations with other VDAXs based on engagement information provided by the PNA object and the other VDAXs.

55. 55. The ecosystem security platform of claim 54, wherein the set of information-theoretically accelerated computationally complex functions is one of cryptography-based functions, non-cryptography functions, and hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

56. 51. The ecosystem security platform of claim 50, wherein the PNA module is configured to establish specific relationships between other VDAXs in the digital ecosystem to which the VDAX belongs based in part on the VDAX's PNA object.

57. The ecosystem security platform of claim 1 , wherein the root DNA module includes an LNA module that forms and constructs the genome correlation object.

58. 58. The ecosystem security platform of claim 57, wherein the LNA module is configured to employ information theory-facilitated genomic processes to establish specific relationships with other VDAXs within their respective digital ecosystems.

59. 58. The ecosystem security platform of claim 57, wherein the link module uses the genome eligibility object to verify link exchange correlation with the second VDAX based on the link.

60. 58. The ecosystem security platform of claim 57, wherein the genome eligibility object is an LNA object that exhibits a particular entropy, and the LNA object enables genome processing based on difference and correlation performed during link exchange.

61. 61. The ecosystem security platform of claim 60, wherein the LNA object is sufficiently correlated with a second LNA object of the second VDAX.

62. 61. The ecosystem security platform of claim 60, wherein the specific entropy of an LNA object is configurable by a community owner.

63. 61. The ecosystem security platform of claim 60, wherein the LNA object is an N-dimensional binary vector that indicates the particular entropy.

64. The ecosystem security platform of claim 60, wherein the LNA module is configured to use a set of computational complexity functions based on information theory to investigate link eligibility correlations with other VDAXs based on the LNA objects and genome engagement cargo provided in each link provided by the other VDAXs.

65. 65. The ecosystem security platform of claim 64, wherein the set of information-theoretically accelerated computationally complex functions is one of cryptographic-based functions, non-cryptographic functions, and hybrid functions including at least one cryptographic-based function and at least one non-cryptographic function.

66. 61. The ecosystem security platform of claim 60, wherein the LNA module is configured to establish specific relationships between other VDAXs in a digital ecosystem to which the VDAX belongs based in part on the VDAX's LNA object.

67. 58. The ecosystem security platform of claim 57, wherein the LNA module is configured to generate respective sets of LNA objects based on the LNA objects of the VDAX, each set of LNA objects being assigned to a respective set of descendant VDAXs, each LNA object exhibiting a particular entropy equal to the entropy of the LNA objects of the VDAX.

68. 58. The ecosystem security platform of claim 57, wherein the LNA module is configured to modify the genome correlation object based on a set of specific instructions using a set of information-theoretically facilitated computationally complex functions, the set of information-theoretically facilitated computationally complex functions being one of cryptography-based functions, non-cryptography functions, and hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

69. 69. The ecosystem security platform of claim 68, wherein the LNA module modifies the genomic correlation object based on a set of instructions received from the ancestral VDAX, and wherein the modifications to the genomic correlation object are used to establish future engagement with the digital ecosystem to which the VDAX belongs, while previously established engagement is not affected.

70. The ecosystem security platform of claim 68, wherein the LNA module modifies the genome correlation object based on a set of instructions received from a second VDAX in the link to obtain a modified genome correlation object, such that the modified genome correlation object is used to determine a link genome engagement factor used to decode the encoded GRI.

71. The ecosystem security platform of claim 1 , wherein the root DNA module includes an XNA module that executes genome processes involving genome correlation objects, including at least one of generating new genome correlation objects and modifying VDAX genome correlation objects.

72. 72. The ecosystem security platform of claim 71, wherein the XNA module is configured to employ information-theoretically facilitated genomic processes to modify the genomic differentiation targets of the VDAX in a manner identical to the second VDAX according to genomic regulatory instructions decoded from the link obtained from the second VDAX.

73. 72. The ecosystem security platform of claim 71, wherein the genome eligible object is an XNA object that exhibits a particular entropy.

74. 74. The ecosystem security platform of claim 73, wherein the XNA object is fully correlated with a second XNA object in a second VDAX.

75. 74. The ecosystem security platform of claim 73, wherein a particular entropy of an XNA object is configurable by a community owner.

76. 74. The ecosystem security platform of claim 73, wherein the XNA object is an N-dimensional binary vector that indicates the particular entropy.

77. 74. The ecosystem security platform of claim 73, wherein the XNA objects are used to establish future differentiation from other VDAXes that possess sufficiently correlated XNA objects.

78. The ecosystem security platform of claim 73, wherein the XNA module is configured to generate respective sets of XNA objects based on the XNA objects of the VDAX, each set of XNA objects being assigned to a respective set of descendant VDAXs, each XNA object exhibiting a particular entropy equal to the entropy of the XNA objects of the VDAX.

79. 74. The ecosystem security platform of claim 73, wherein the XNA module is configured to modify the XNA based on a specific set of instructions using a set of information-theoretically facilitated computational complexity functions.

80. 80. The ecosystem security platform of claim 79, wherein the XNA module updates the XNA based on a set of instructions received from a predecessor VDAX such that the updated XNA is used to establish future differentiation with respect to other VDAXs within the digital ecosystem to which the VDAX belongs.

81. 81. The ecosystem security platform of claim 80, wherein a second VDAX cannot establish future differentiation with the VDAX unless the second VDAX possesses sufficiently correlated, persistently updated XNAs.

82. 80. The ecosystem security platform of claim 79, wherein the set of information-theoretically accelerated computationally complex functions is one of cryptography-based functions, non-cryptography functions, and hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

83. 10. The ecosystem security platform of claim 1, further comprising an authentication module that prosecutes a secure genome-based engagement correlation with respect to a second VDAX according to an information theory-accelerated computational complexity function, wherein the information theory-accelerated computational complexity function is one of a cryptography-based function, a non-cryptography function, and a hybrid function including at least one cryptography-based function and at least one non-cryptography function.

84. The ecosystem security platform of claim 1 , further comprising a master integrity controller including a genome process controller, an authorization module, and an engagement instance module, the genome process controller having a master controller genome dataset assigned thereto.

85. 85. The ecosystem security platform of claim 84, wherein the genome process controller is configured to engage with one or more platform modules to authenticate and verify the integrity of the one or more platform modules.

86. 86. The ecosystem safety platform of claim 85, wherein the genomic process controller verifies the integrity and authenticates one or more platform modules based on a master controller genomic dataset and a set of computationally complex functions.

87. 87. The ecosystem security platform of claim 86, wherein the genome process controller verifies and authenticates the integrity of one or more platform modules without determining any processes or functions performed by the one or more platform modules.

88. 87. The ecosystem security platform of claim 86, wherein the genome process controller is further configured to verify and authenticate the integrity of any underlying operational processes and functions connecting one or more platform modules using a set of computationally complex functions.

89. 89. The ecosystem safety platform of claim 88, wherein the genome process controller is further configured to verify, disqualify, or initiate modification of one or more platform modules and underlying operational processes and functions.

90. The authorization module confirms or rejects the operational settings of the other VDAX; 85. The ecosystem security platform of claim 84, configured to build an ecosystem based on genome controller genome data and a set of information-theoretically accelerated computational complexity functions, comprising:

91. 91. The ecosystem security platform of claim 90, wherein in response to determining to deny the operational configuration of the other VDAX, the authorization module disqualifies or initiates a change to the operational configuration of the other VDAX.

92. 85. The ecosystem security platform of claim 84, wherein the engagement instance module is configured to track security instances in the VDAX digital ecosystem according to a set of one or more engagement tracking policies that define one or more definitions of the security instances.

93. 93. The ecosystem security platform of claim 92, wherein the engagement instance module is further configured to determine the number of security instances in the VDAX digital ecosystem according to a set of one or more engagement accounting policies, each defining how a security instance is counted.

94. 94. The ecosystem security platform of claim 93, wherein the engagement instance module is further configured to report the number of security instances in the digital ecosystem to another VDAX in accordance with a set of one or more engagement reporting policies that respectively define how the security instances are reported, to which VDAX the security instances are reported, and how frequently the security instances are reported.

95. The ecosystem security platform of claim 1, wherein VDAX is a member of a digital ecosystem.

96. 96. The ecosystem security platform of claim 95, wherein the digital ecosystem is an enterprise information technology system.

97. 96. The ecosystem security platform of claim 95, wherein the digital ecosystem is a computing device and the descendant VDAXs each correspond to a hardware component and a digital component of the computing device.

98. 96. The ecosystem security platform of claim 95, wherein the digital ecosystem is a traffic grid.

99. 96. The ecosystem security platform of claim 95, wherein the digital ecosystem is a home network.

100. 96. The ecosystem security platform of claim 95, wherein the digital ecosystem is a classified computing infrastructure.

101. A system for performing genome security-related control of a digital ecosystem, comprising: and an Ecosystem VDAX executed by a processing system associated with a digital ecosystem owner, the Ecosystem VDAX being comprised of an Ecosystem instance of an Ecosystem Security Platform, the Ecosystem VDAX comprising: maintaining an ancestral genomic dataset corresponding to the digital ecosystem, the ancestral genomic dataset including one or more different digitally generated ancestral genomic data objects, and generating a plurality of respective descendant genomic datasets based on the ancestral genomic dataset, each ancestral genomic data object exhibiting a respective particular entropy, each descendant genomic dataset including one or more different descendant genomic data objects each derived from the one or more digitally generated ancestral genomic data objects and exhibiting a respective particular entropy of the ancestral genomic data objects from which it was derived; For each descendant genome dataset, assigning the descendant genomic dataset to each descendant VDAX of the plurality of descendant VDAXs, and the descendant VDAXs establishing unique, non-recurrent engagement with other descendant VDAXs in the digital community based on the respective descendant genomic dataset assigned to the descendant VDAX without further interaction from the ecosystem VDAXs; and A system configured to control the genomic topology of a digital ecosystem by selectively updating one or more of the progeny genomic datasets to affect the ability of a particular progeny VDAX to engage with other VDAXs in the digital ecosystem. thing.

102. 102. The system of claim 101, wherein the ancestral genome dataset comprises ancestral genome differentiation objects, and each ancestral genome dataset comprises a respective ancestral genome differentiation object.

103. 103. The system of claim 102, wherein pairs of descendant VDAXs from the plurality of descendant VDAXs can exchange virtual binary language scripts (VBLS) only if the respective descendant genome differentiation objects of the pairs of descendant VDAXs are sufficiently correlated.

104. The system of claim 103, wherein if a first descendant genome differentiation object of a first descendant VDAX in the set of descendant VDAXs is updated and a second descendant genome differentiation object of a second descendant VDAX in the set of descendant VDAXs is not updated, the set of descendant VDAXs is prevented from future VBLS replacement.

105. 104. The system of claim 103, wherein the ancestor genome differentiation object and each of the descendant genome differentiation objects are XNA objects.

106. The digital ecosystem: a static ecosystem, wherein the ecosystem platform is configured according to a prescribed architecture; an interactive ecosystem, wherein the ecosystem platform is configured according to a free-form architecture; or 106. The system of claim 105, wherein the ecosystem platform is at least one of: a dynamic ecosystem configured according to a dynamic state spontaneous architecture.

107. 107. The system of claim 106, further comprising a set of one or more enclave VDAXs, each enclave VDAX corresponding to a respective digital enclave of the digital ecosystem, the enclave VDAX being assigned a respective enclave-specific XNA object that controls the genome topology of the respective enclave.

108. 108. The system of claim 107, wherein each digital enclave includes one or more descendant VDAXs each representing one or more respective cohorts admitted to the digital enclave, and wherein each descendant VDAX included in each digital enclave allocates a descendant enclave-specific XNA object that is derived from the enclave-specific XNA object of the enclave VDAX and that is sufficiently correlated with each descendant enclave XNA object of other descendant VDAXs included in the digital enclave.

109. 109. The system of claim 108, wherein each enclave VDAX controls membership to each corresponding digital enclave by assigning enclave-specific XNA objects to a cohort of digital enclaves.

110. 109. The system of claim 108, wherein each enclave VDAX further assigns a respective enclave genome correlation object derived from an ancestral correlation object of the ancestral genome dataset.

111. 111. The system of claim 110, wherein for each digital enclave, each descendant VDAX of the digital enclave is assigned a respective descendant enclave-specific genome correlation object derived from the enclave from each enclave genome correlation object of the digital enclave's enclave VDAX.

112. 112. The system of claim 111, wherein each descendant VDAX assigns its respective enclave-specific genome correlation object directly from the enclave VDAX of the digital enclave.

113. 112. The system of claim 111, wherein each descendant VDAX assigns its respective enclave-specific genome correlation object directly from the ecosystem VDAX.

114. 112. The system of claim 111, wherein each descendant VDAX uses its respective descendant enclave-specific genome correlation object to generate links that establish respective unique non-recursive engagements with other descendant VDAXs formed with respect to the respective digital enclave.

115. 115. The system of claim 114, wherein each link spawned by a descendant VDAX provides unique genomic regulatory instructions that define how the link hosting the descendant VDAX modifies its enclave-specific XNA object to generate non-recurrent VBLS that can only be deciphered by the descendant VDAX.

116. 112. The system of claim 111, wherein each descendant VDAX uses its respective descendant enclave-specific genomic correlation object to host links provided by other descendant VDAXs in the digital ecosystem, and other descendant VDAXs provide links to the descendant VDAX to establish unique, non-recurrent engagement with the descendant VDAX for its respective digital enclave.

117. Each link hosted by a descendant VDAX provides unique genome regulatory instructions that define how progeny VDAX modify it, The system of claim 116, wherein the enclave-specific genome differentiation object generates a non-regenerating VBLS that can only be deciphered by other descendant VDAXs that provided the link.

118. 108. The system of claim 107, wherein the enclave VDAX controls the genomic network topology by selectively updating descendant enclave-specific genomic data objects of a subset of descendant VDAXs that participate in the digital enclave.

119. 108. The system of claim 107, wherein the enclave VDAX controls the genomic network topology of the digital enclave without requiring modifications to the physical network topology of the digital enclave.

120. 107. The system of claim 106, wherein the digital ecosystem comprises multiple genome topologies overlaying one or more physical network topologies, the multiple genome topologies existing simultaneously and interoperably.

121. 107. The system of claim 106, wherein the ecosystem VDAX builds and controls a genome network topology that supports applications with dynamic state attributes.

122. 122. The system of claim 121, further comprising a set of one or more Enclave VDAXs, each Enclave VDAX corresponding to a respective digital enclave of the digital ecosystem and assigned a respective enclave-specific genomic dataset that controls the portion of the genomic network topology that the Enclave VDAX is responsible for ecosystem-specified functions and processes of the digital ecosystem.

123. The system of claim 122, further comprising a set of cohorts VDAX participating in the digital ecosystem, the set of cohorts VDAX comprising one or more cohorts each controlling a respective portion of the genome network topology responsible for specific ecosystem-specific and / or enclave-specific functions and processes.

124. 124. The system of claim 123, wherein interactions by the set of cohort VDAXs are controlled by each cohort genomic dataset assigned to each cohort VDAX in the set of cohort VDAXs.

125. 125. The system of claim 124, wherein the set of progeny VDAXs comprises the set of cohort VDAXs.

126. The cohort genomic data set for each cohort in the cohort VDAX set is one or more cohort genomic eligibility objects containing one or both of one unique CNA object and one unique PNA object; one or more Cohort Genome Correlation Objects containing one or more LNA objects, each LNA object corresponding to a respective enclave to which the Cohort VDAX is enrolled; and 125. The system of claim 124, comprising one or more cohort genome differentiation objects including one or more XNA objects, each XNA object corresponding to a respective enclave in which the cohort VDAX is enrolled.

127. 122. The system of claim 121, wherein the digital ecosystem is a dynamic ecosystem and the ecosystem security platform is configured according to a voluntary architecture that preserves its operational integrity regardless of how frequently one or more metric states of the dynamic ecosystem are updated.

128. The system of claim 127, wherein the ecosystem VDAX and descendant VDAX collectively control the genome network topology in response to specific dynamic metric states.

129. 128. The system of claim 127, which supports multiple genomic network topologies overlaid on a simultaneous physical network topology.

130. 128. The system of claim 127, wherein the genomic digital network topology is constructed to achieve a controlled level of interoperability.

131. 103. The system of claim 102, wherein the ancestor genome differentiation object and each of the descendant genome differentiation objects are ZNA objects.

132. 132. The system of claim 131, wherein the digital ecosystem is a virtual trusted execution domain implemented for a device where descendant VDAXs correspond to respective components of the device, and the components of the device may include one or more hardware components and one or more digital components.

133. 132. The system of claim 131, wherein a descendant VDAX from the plurality of descendant VDAXs is configured to perform component binary isolation (CBI) with respect to the virtual trusted execution domain.

134. 102. The system of claim 101, further comprising a set of progeny VDAX.

135. 135. The system of claim 134, wherein each descendant VDAX comprises a respective descendant instance of an ecosystem security platform such that each descendant instance of the ecosystem security platform is configured with a respective set of functionally matched modules each configured to perform one or more information theory-accelerated computational complexity functions.

136. 135. The system of claim 135, wherein the set of modules of each descendant instance of the ecosystem security platform includes a DNA module configured to manage a genomic dataset of the descendant VDAX and perform a set of genomic processes based on the genomic dataset.

137. 136. The system of claim 135, wherein the set of modules includes a link module configured to facilitate secure exchange of a link with another VDAX, facilitating unique bi-symmetric engagement.

138. 136. The system of claim 135, wherein the set of modules includes a sequence mapping module configured to genomically process a sequence to derive a genomic engagement factor having a particular entropy, the genomic engagement factor being used to encode a digital object in a non-recurrent manner, the sequence being at least one of a public sequence or a private sequence.

139. 139. The system of claim 138, wherein the set of modules includes a binary conversion module configured to encode digital objects into VBLS objects based on the genomic engagement factors determined by the sequence mapping module, each VBLS object including the encoded digital object and metadata indicating public or private sequences used to generate a respective genomic engagement factor used to encode the encoded digital object.

140. 139. The system of claim 138, wherein the binary conversion module is further configured to decode a received encoded digital object included in a received VBLS object based on each recreated genome engagement factor.

141. 102. The system of claim 101, wherein an ecosystem instance of the ecosystem security platform has a respective set of modules configured to perform a respective set of computationally complex information-theoretically driven computationally complex functions.

142. 142. The system of claim 141, wherein each set of information theory accelerated based computationally complex functions is selected from cryptographic-based functions, non-cryptographic functions, and hybrid functions including at least one stage performed using a cryptographic-based function and at least one stage performed using a non-cryptographic function.

143. 143. The system of claim 142, wherein the set of modules of the ecosystem instance includes a root DNA module that manages the ancestral genomic dataset and generates the descendant genomic dataset.

144. 102. The system of claim 101, wherein the digital ecosystem is a cloud services system.

145. 102. The system of claim 101, wherein the digital ecosystem is an enterprise information technology system.

146. 102. The system of claim 101, wherein the digital ecosystem is a computing device and the descendant VDAXs each correspond to a hardware component and a digital component of the computing device.

147. 102. The system of claim 101, wherein the digital ecosystem is a traffic grid.

148. 148. The system of claim 147, wherein the traffic grid is an air traffic control grid.

149. 148. The system of claim 147, wherein the traffic grid is an autonomous vehicle traffic grid.

150. 102. The system of claim 101, wherein the digital ecosystem is a classified computing infrastructure.

151. 1. A method for managing a set of digital entities in a digital ecosystem, comprising: generating an ancestral genomic dataset having a particular entropy by a processing system of an Ecosystem VDAX, wherein the ancestral genomic dataset is assigned to the Ecosystem VDAX; generating, by a processing system, a plurality of different progeny genome data sets, each of which exhibits a particular entropy; and for each descendant of the plurality of different descendant genomic datasets, assigning, by a processing system, the descendant genomic dataset to a respective digital entity of the set of digital entities, wherein the set of digital entities is enabled to achieve precise control of differences and correlations based on the respective descendant genomic datasets of each digital entity.

152. 152. The method of claim 151, wherein any pair of digital entities in the set of digital entities is configured to verify a correlation of the respective genomic datasets of the pair of digital entities, and based on the verified correlation of the progeny genomic datasets of the pair of digital entities, distinguish the respective progeny genomic dataset from any other progeny genomic dataset to form a unique non-repeated relationship within the digital community.

153. 153. The method of claim 152, wherein the pair of digital entities are each configured to independently verify correlation using a particular set of information-theoretically accelerated computational complexity functions.

154. 154. The method of claim 153, wherein the set of information-theoretically accelerated computationally complex functions is one of cryptography-based functions, non-cryptography functions, and hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

155. 154. The method of claim 153, wherein each digital entity of the set of digital entities is configured to independently distinguish a respective progeny genomic data set using a second set of information-theoretically facilitated computational complexity functions.

156. 156. The method of claim 155, wherein the second set of computationally complex functions is one of cryptography-based functions, non-cryptography functions, and hybrid functions including at least one cryptography-based function and at least one non-cryptography function.

157. 153. The method of claim 152, wherein in response to forming a unique non-recurrent relationship, the pair of entities engage by generating and exchanging a unique non-recurrent virtual binary language script (VBLS) decipherable only by the pair of entities based on differentiated progeny genomic data, wherein the pair of entities

158. 158. The method of claim 157, wherein the VBLS is comprised of encoded digital objects that hold information-theoretically facilitated genomic attributes of the genomic dataset for each of the set of digital entities.

159. 152. The method of claim 151, wherein the specified entropy is a configurable level of entropy defined by a community owner associated with the ecosystem VDAX.

160. 152. The method of claim 151, wherein the digital entities collectively enable virtual authentication, virtual affiliation, and virtual agility.

161. 152. The method of claim 151, wherein each progeny genome dataset comprises a genome correlation object exhibiting a particular entropy and a genome differentiation object exhibiting a particular entropy.

162. 163. The method of claim 162, wherein each progeny genome dataset comprises a respective genome eligible object that exhibits a particular entropy.

163. 152. The method of claim 151, wherein the digital ecosystem comprises one or more digital enclaves formed based on respective mutual identities of interest represented by controlled differences and correlations in the progeny genomic dataset.

164. 164. The method of claim 163, wherein each digital enclave comprises one or more cohorts that share respective mutual benefits represented by controlled differences and correlations in progeny genomic datasets.

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