CipherGenix-Based Ecosystem Security Platform

Abstract

The present disclosure relates to CipherGenics (CG)-enabled security platforms and corresponding digital genome constructions that undergo digital modification and reconfiguration by computationally complex functions and processes without losing genome integrity while exhibiting controlled entropy. These constructions enable the formation of comprehensively secure hyper-scalable digital ecosystems, enclaves, and / or digital cohorts with mutual identities of interest, and enable application-specific security architectures based on genome network topologies interoperable with modern applications and network stacks. In some embodiments of the present disclosure, the security platform may be configured to facilitate secure data exchange between members of the ecosystem. In some embodiments of the present disclosure, the security platform is configured to facilitate a Material Data Chain (MDC).

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 145,860, filed February 4, 2021, and U.S. Provisional Application No. 63 / 229,348, filed August 4, 2021, the entire disclosures of which are incorporated by reference.

[0002] The present disclosure relates to Cyphergenics (CG)-enabled security platforms and corresponding digital genome constructions that exhibit controlled entropy while undergoing digital modification and reconfiguration through computationally complex functions and processes without losing genome integrity. These constructions enable the formation of comprehensively secure, hyperscalable digital ecosystems, enclaves, and / or digital cohorts with mutual identity of interests, enabling application-specific security architectures based on genomic network topologies interoperable with modern applications and network stacks. In some embodiments of the present disclosure, the security platform may be configured to facilitate secure data exchange between members of the ecosystem. In some aspects of the present disclosure, the security platform is configured to facilitate virtual trusted execution domains, whereby the processing environment is configured to perform CG-based encoding and decoding of instructions and program data. In some aspects of the present disclosure, the security platform is configured to facilitate a material data chain (MDC). [Background technology]

[0003] The requirement to distinguish between the noble and the nefarious became more urgent as the ARPAnet's community of connected nodes breached its boundaries into 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-response technologies (firewalls, analytics, forensics, PKI, proxies, monitoring, etc.) had already been relegated to patrolling the network perimeter and providing rapid restoration services.

[0004] Experts agree that cryptography is the only provable security solution for a machine-connected world. Research into quantum cryptography, homomorphic encryption, and obfuscated cryptography has attracted significant investment but has not had the impact expected. Nevertheless, hyperscalability, the most essential yet extremely complex aspect of all cryptography, remains untouched. Despite active efforts to update PKI to a post-quantum state, its linear scalability persists.

[0005] Applicant has developed and disclosed herein CipherGenics (CG) technology that comprehensively solves the previously intractable hyperscalability dilemma. As will be described, CipherGenics enables unlimited deployment of cryptography-based digital ecosystem security with minimal overhead and bandwidth, while fully preserving interoperability. Importantly, hyperscalability directly facilitates functional homomorphic cryptography and functional indistinguishable obfuscation. Summary of the Invention

[0006] The artificial dichotomy between attacks on cyberinfrastructure and attacks on privacy has been and continues to be misguided, as both leverage the same digital ecosystem (a machine-connected world) and digital monoculture (everything interoperates with everything). Security solutions, despite their impressive adaptation and extension of vintage security technologies and techniques, remain pedestrian kluges—good at post-mortem forensics and poor at prevention. The limited ability of these technologies to prevent sabotage, espionage, intrusion, piracy, and privacy violations (whether covert or covert surveillance) is well-known. The ever-increasing surface and power of digital attacks, met with weaponized malware, processor exploitation, and ultimately new variants of quantum-computer-inspired crypto-cracking and artificial intelligence-inspired destructive algorithms, heralds new levels of devastation.

[0007] Noble engagements have fostered a previously unimaginable virtualization of products, services, and knowledge, redefining efficiency and efficacy. From within the same network-centric campus, nefarious engagements facilitate devastating cyberattacks (e.g., sabotage, espionage, interloping, piracy) and widespread attacks on privacy (e.g., covert mass surveillance, profiling, assimilation). What they share is a common machine language and inherent hyperscalability essential to their network-centric missions, making the noble and the nefarious virtually indistinguishable.

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

[0009] Reconstructing the digital ecosystem and the digital monoculture remains unrealistic, haphazardly disruptive, and financially uneconomical. While technologies that can modify common machine codes based on quantum-proof cryptography offer highly effective security, they also destroy the hyperscalability essential for the interoperability of the digital monoculture.

[0010] CipherGenics (CG), an entirely new technology based on computationally complex genome construction, liberates modern cryptography from its esoteric yet powerful computationally complex foundations. In embodiments, CG enables virtual boundarylessness by generating information-theoretically constructed genome constructions that exhibit directly controllable computational complexity similar to biochemically efficient constructions. Importantly, CG digital renderings preserve the boundaryless properties of biochemical systems while significantly expanding the range of their inherent differences and correlations. CG preserves the ability to strategically control different digital DNA-based genome constructions without compromising computational integrity. Figure 1 illustrates attributes of a CipherGenics-based digital ecosystem in relation to the associated attributes of organic ecosystems and modern digital ecosystems, according to some embodiments of the present disclosure.

[0011] In embodiments, unique genomic and cryptographic properties, fused with information theoretical 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 Trusted Domains of Execution—powerful properties that go far beyond security.

[0012] In embodiments, CipherGenics enables hyperscalability of specific digital ecosystems, enclaves, and cohorts, with mutual identities of interest (related and regulated by digital DNA) forming the active basis of their engagement. Ecosystems, enclaves, and cohorts residing in these domains engage based on hyperscalable digital data objects and digital coder objects that exhibit unique, non-recurring, computationally quantum-proof attributes that reflect the mutual identities of interest, called Virtual Binary Language Scripts (VBLS), and which they share openly with malicious intent.

[0013] While CipherGenix (CG) technology can be supported by a wide range of digitally enabled 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 CipherGenix Ecosystem Security Platform (CG-ESP) can be comprised of modules that control specific computational and genome construction and digital DNA regulatory functions. This adaptability is crucial given that CipherGenix functionality may be rendered cryptographically, non-cryptographically, 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 individual genome information theory-enabled construction and regulatory processes and functions to be reconstructed and incrementally improved or modified without compromising hyperscalability, and they also enable computational and functional innovation across CipherGenix application-ready attributes.

[0015] With few exceptions, security applications must tolerate the network configurations in which they execute transactions and the vulnerabilities these networks often induce (e.g., NAT bypass of IP-SEC security to expand exhausted IP-IV addresses). CipherGenix VBLS attributes enable powerful new security application-centric genome network topologies to operate concurrently, interoperably, and on-demand on existing network configurations. CipherGenix enables many security-centric genome network topologies, including direct, spontaneous, ephemeral, and interledger architectures. Figure 2 illustrates one example of this adaptability, showing a CipherGenix-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 CipherGenix-facilitated genome architecture for a digital ecosystem that can result from such applications.

[0016] In embodiments, the scope of CipherGenix's information-theoretic enabled genome construction allows digital cohorts to be born as the offspring of specific enterprises and / or enclaves prior to their own conception. In embodiments, CipherGenix-enabled digital ecosystems can be gnomically rendered flat or hierarchical, with or without an orientation to order, such as time. In embodiments, CipherGenix cohorts can act as carriers of Cambrian genomes, storing instructions for the subsequent construction of their own species-specific ecosystems, enclaves, and offspring (reverse reproduction), which must undergo genomic adjustments to achieve correlation and differentiation.

[0017] The differences between the implementation and practice of ciphergenics detailed in this patent and the exploration of postmodern (quantum proof) cryptography are summarized below and illustrated in Figure 3. Even if successful, contemporary efforts to defeat quantum computer cryptanalysis will only maintain the status quo even as network-centric security challenges continue to escalate significantly and significantly.

[0018] In embodiments, CipherGenics-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 implementations of CipherGenics-based technology and security platforms applied to 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 each ecosystem.

[0020] According to some embodiments of the present disclosure, a device is disclosed. In some embodiments, the device includes a storage system including one or more computer-readable storage media, the storage system storing a computer program comprising a set of encoded computer-executable instructions, a genomic dataset including a genomic differentiation object used to encode the set of encoded computer-executable instructions, and genomic tailoring instructions (GRI) corresponding to the computer program, the genomic tailoring instructions used to encode the set of encoded computer-executable instructions. The device further includes a processing system including a VDAX and a set of processing cores. The VDAX is configured to receive encoded instructions to be executed from the set of encoded computer-executable instructions and decode the encoded instructions into decoded executable instructions based on the modified genomic differentiation object and a sequence extracted from metadata associated with the encoded instructions. In these embodiments, the modified genomic differentiation object is modified from the genomic differentiation object using the GRI. The set of processing cores is configured to receive the decoded executable instructions from the VDAX and execute the decoded executable instructions. In some embodiments, the genomic differentiation object is a ZNA object.

[0021] In some embodiments, the VDAX is configured to modify the genomic differentiation object based on the GRI using a set of computational functions to obtain a modified genomic differentiation object. For each coded instruction executed by the processing device, the VDAX is further configured to: obtain a genomic engagement factor (GEF) corresponding to each coded computer-executable instruction; decode each coded instruction using the GEF corresponding to each executed coded instruction to obtain a respective decoded executable instruction; and output each decoded instruction to a processing core of the set of processing cores. In these embodiments, the GEF is generated based on the sequence extracted from the metadata corresponding to each coded computer-executable instruction, the GRI, and the modified genomic data object. In some of these implementations, decoding each coded instruction using the GEF includes decoding each coded instruction using the GEF as a key to obtain a respective decoded instruction. In some implementations, decoding each coded instruction using the GEF includes disambiguating each decoded instruction from each coded instruction using the respective GEF. In some of these implementations, disambiguating the respective decoded instructions from the respective encoded instructions includes performing an XOR operation using the encoded instructions and the GEF as inputs to the XOR operation. In some implementations, the respective encoded instructions are encoded with different respective GEFs, such that the respective GEFs are determined based on respective sequences extracted from respective metadata that uniquely correspond to the respective encoded instructions. In some of these implementations, the respective metadata that uniquely correspond to the respective encoded instructions includes one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata.In some of these implementations, each sequence used to generate a respective GEF used to decode each encoded instruction is a set of bits extracted from a bit representation of the respective metadata. In some implementations, the set of encoded computer-executable instructions includes a subset of instructions, and each subset of the plurality of subsets is decoded with a respective GEF generated based on a respective sequence extracted from metadata corresponding to each respective subset of computer-executable instructions. In some of these implementations, the respective metadata from which each sequence is extracted includes one or more of random access memory (RAM) metadata corresponding to the subset, application metadata corresponding to the subset, operating system application metadata corresponding to the subset, and file system metadata corresponding to the subset. In some embodiments, obtaining a GEF corresponding to each encoded computer-executable instruction includes sequence mapping the sequence to a modified genome data object to obtain a GEF. In some of these implementations, sequence mapping the sequence to the modified genome data object includes generating a sequence transformation vector (SCV) based on the sequence and the GRI; mapping the modified genome differentiation object based on the SCV to obtain a mapped modified genome differentiation object; and generating a GEF based on the mapped modified genome differentiation object.

[0022] In some implementations, the storage system stores a second computer program including a second set of encoded computer-executable instructions and a second GRI corresponding to the second computer program, the second GRI being used to decode the second set of encoded computer-executable instructions. In some of these implementations, the second GRI is different from the GRI used to decode the set of encoded computer-executable instructions. In some of these embodiments, the storage device further stores a second genomic differentiation object used to encode and decode the second set of encoded computer-executable instructions, the second genomic differentiation object being different from the genomic differentiation object used to encode and decode the set of encoded computer-executable instructions. In some embodiments, the genomic differentiation object is modified using the second GRI to obtain a second modified genomic differentiation object, and the second modified genomic differentiation object is used to decode the second set of encoded computer-executable instructions.

[0023] In some embodiments, the processing system is a processing device and the VDAX is a dedicated core of the processing device. In some implementations, the VDAX is a field programmable gate array in electrical communication with the set of processing cores. In some implementations, the VDAX is a microprocessor in electrical communication with the set of processing cores. In some implementations, the VDAX is included in an operating system executed by the set of processing cores.

[0024] In implementations, the device is one of a server computing device, a mobile computing device, a personal computing device, a vehicle, an Internet of Things device, a sensor device, a video recording device, a wearable computing device, a smart appliance, a network router, a gaming device, etc. In implementations, the computer program is a software application, a middleware application, a firmware application, and / or an operating system.

[0025] According to some embodiments of the present disclosure, a method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes retrieving a genome differentiation object corresponding to a set of one or more computer programs, including a first computer program, the first computer program including a first set of encoded computer-executable instructions, the genome differentiation object including a binary vector of a particular length. The method further includes modifying the genome differentiation object based on a first genome adjustment instruction (GRI) to obtain a first modified genome differentiation object, where the first GRI was used to encode the first set of encoded executable instructions of the first computer program. The method also includes obtaining first coded instructions to be executed from a first set of coded computationally executable instructions of a first computer program; obtaining a first sequence from first metadata associated with the first coded instructions; generating a first genome engagement factor (GEF) based on the first sequence and the first modified genome differentiation object; decoding the first coded instructions with the first GEF to obtain first decoded instructions; and executing the first decoded instructions. In some embodiments, the genome differentiation object is a ZNA object.

[0026] In some embodiments, the method further includes obtaining second encoded instructions to be executed from a first set of encoded computer-executable instructions of the first computer program; obtaining a second sequence from second metadata associated with the second encoded instructions; generating a second GEF based on the second sequence and the modified genome differentiation object; decoding the second encoded instructions using the second GEF to obtain second decoded instructions; and executing the second decoded instructions.

[0027] In some embodiments, the method further includes modifying the genome differentiation object with a second GRI corresponding to a second computer program of the set of computer programs to obtain a second modified genome differentiation object, the second computer program comprising a second set of encoded computer-executable instructions. In these embodiments, the method further includes obtaining third encoded instructions to be executed from the second set of encoded computer-executable instructions of the second computer program; obtaining a third sequence from third metadata associated with the third encoded instructions; generating a third GEF based on the third sequence and the second modified genome differentiation object; decoding the third encoded instructions using the third GEF to obtain third decoded instructions; and executing the third decoded instructions. In some of these implementations, the genome differentiation object is assigned to both the first computer program and the second computer program. In some of these implementations, the first GRI corresponds only to the first computer program and the second GRI corresponds only to the second computer program. In some embodiments, the first computer program and the second computer program, and any other computer programs in the set of computer programs, are distributed by the same entity. In some of these embodiments, the entity assigns genomic differentiation objects to the first and second computer programs. In some embodiments, a VDAX on the device assigns genomic differentiation objects to the first and second computer programs. In some of these embodiments, the VDAX generates the first GRI and the second GRI.

[0028] In some embodiments, the method further includes installing the first computer program before decoding the first coded instructions and the second coded instructions. In some of these embodiments, installing the first computer program includes obtaining a set of uncoded computer-executable instructions including the first computer program; allocating memory on a computer-readable storage medium of the device to the first computer program; generating a first GRI and associating the first GRI with the first computer program; modifying the genomic differentiation object based on the first GRI to obtain a modified genomic differentiation object; and generating the first set of coded computer-executable instructions. In generating the first set of coded instructions, the method includes, for each uncoded instruction in the set of uncoded computer-executable instructions, determining a respective sequence from respective metadata associated with each uncoded instruction; generating a respective GEF based on the respective sequence and the modified genomic differentiation object; encoding each uncoded instruction using the respective GEF to obtain a respective coded instruction of the first set of coded computer-executable instructions; and storing each coded instruction in a memory location in the allocated memory. In some of these implementations, the respective metadata from which the respective sequences are extracted includes one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata.

[0029] In some implementations, the execution environment is a processing device that includes an executable separate component VDAX, which is a dedicated core of the processing device that decodes the encoded instructions, and a set of processing cores that execute the decoded instructions.

[0030] In implementations, the device is one of a server computing device, a mobile computing device, a personal computing device, a vehicle, an Internet of Things device, a sensor device, a video recording device, a wearable computing device, a smart appliance, a network router, a gaming device, etc. In implementations, the first computer program is a software application, a middleware application, a firmware application, and / or an operating system.

[0031] According to some embodiments of the present disclosure, a method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes obtaining a genome differentiation object corresponding to a set of one or more computer programs including the computer program; modifying the genome differentiation object based on genome adjustment instructions (GRIs) to obtain a modified genome differentiation object; and executing first executable instructions of the computer programs. In an embodiment, executing the first executable instructions includes: obtaining first encoded data input to the first executable instructions; extracting a first sequence from first metadata associated with the first encoded data; generating a first genome engagement factor (GEF) based on the first sequence, the GRI, and the modified genome differentiation object; decoding the first encoded data based on the first GEF to obtain first decoded data; and executing the first executable instructions using the first decoded data. The method further includes executing second executable instructions of the computer program, where executing the second executable instructions includes: obtaining second encoded data input to the second executable instructions; extracting a second sequence from second metadata associated with the second encoded data; generating a second GEF based on the second sequence, the GRI, and the modified genome differentiation object; decoding the second encoded data based on the second GEF to obtain second decoded data; and executing the second executable instructions using the second decoded data. In some embodiments, the genome differentiation object is a ZNA object. In some of these embodiments, the ZNA object is an N x M binary matrix. In some of these embodiments, a VDAX of the device generates and assigns the ZNA object to the computer program.

[0032] In some embodiments, executing the first executable instructions further includes: obtaining output data as a result of execution of the first executable instructions; generating a third GEF based on a third sequence corresponding to the output data, the GRI, and the modified genomic differentiation target; and converting the output data into third encoded data based on the third GEF. In some of these embodiments, the method further includes writing the third encoded data to memory. In some of these embodiments, the third sequence is extracted from metadata corresponding to the output data, where the metadata includes one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata. In some embodiments, the method further includes executing third executable instructions of the computer program, wherein the execution of the third executable instructions is achieved by: obtaining third encoded data input to the third executable instructions; extracting a third sequence from a value representing metadata corresponding to the output data; generating a third GEF based on the third sequence, the GRI, and the modified genome differentiation object; decoding the third encoded data based on the third GEF to obtain output data; and executing the first executable instructions using the output data obtained as a result of the execution of the first executable instructions.

[0033] In some implementations, the first metadata from which the first sequence is extracted and the second metadata from which the second sequence is extracted include one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata.

[0034] In some embodiments, generating the first GEF includes: generating a sequence transformation vector (SCV) based on the first sequence and the first GRI; mapping the modified genomic differentiation object based on the SCV to obtain a mapped modified genomic differentiation object; and generating the first GEF based on the mapped modified genomic differentiation object. In some of these embodiments, generating the SCV includes combining the first sequence and the GRI. In some of these embodiments, generating the SCV further includes applying one or more computational functions to the combination of the first sequence and the GRI to obtain the SCV.

[0035] In some implementations, decoding the first encoded data includes decoding the first encoded data using a first GEF, and decoding the second encoded data includes decoding the second encoded data using a second GEF.

[0036] In some implementations, decoding the first encoded data includes disambiguating the first encoded data using a first GEF, and disambiguating the second encoded data includes decoding the second encoded data using a second GEF.

[0037] In implementations, the device is one of a server computing device, a mobile computing device, a personal computing device, a vehicle, an Internet of Things device, a sensor device, a video recording device, a wearable computing device, a smart appliance, a network router, a gaming device, etc. In implementations, the first computer program is a software application, a middleware application, a firmware application, and / or an operating system.

[0038] According to some embodiments of the present disclosure, a method for validating an unverified portion of a material data chain (MDC) maintained by a creator cohort in a ledger-based digital ecosystem is disclosed. In embodiments, the method includes receiving, by a processing system of the validation cohort, an unverified portion of the MDC from the creator cohort, the unverified portion of the MDC including a set of contiguous material data blocks (MDBs) generated by the creator cohort. Each MDB includes respective material data stored therein, respective metadata associated with the MDB, and a creator-generated validation value generated by the creator cohort for the validation cohort. The method further includes obtaining, by the processing system of the validation cohort, a genomic differentiation object assigned to the validation cohort for the ledger-based ecosystem and a first genomic reconciliation instruction (GRI) used by the creator cohort to generate the creator-generated validation value. The method further includes modifying, by the processing system of the validation cohort, the genomic differentiation object based on the first GRI using a set of computational functions to obtain a modified differentiation object. For each MDB in the unverified portion of the MDC, the method further includes generating a genomic engagement factor (GEF) based on the sequence extracted for the MDB, the corrected genomic differentiation object, and the first GRI; determining a verifier-generated verification value based on the MDB, the MDBs preceding the MDB in the MDC, and the GEF; determining whether the verifier-generated verification value matches a creator-generated verification value in the MDB; and, in response to determining that the verifier-generated verification value matches the creator-generated verification value, generating a verification record corresponding to the MDB indicating that the MDB has been verified by the validation cohort. The unverified portion of the MDC is verified by the validation cohort when each MDB in the unverified portion of the MDC is verified by the validation cohort.

[0039] In some embodiments, the creator-generated validation value for each MDB is generated by the creator cohort based on the MDB, an MDB that precedes the MDB in the MDC, and a second modified genomic differentiation object derived by the creator cohort by modifying a second genomic differentiation object assigned to the creator cohort using a second GRI, where the second GRI matches the first GRI.

[0040] In some of these embodiments, the creator-generated verification value of each MDB matches the verifier-generated verification value generated by the validation cohort only if the genomic differentiation object assigned to the validation cohort sufficiently correlates with the second genomic differentiation object assigned to the creator cohort. In some implementations, the creator-generated verification value of each MDB matches the verifier-generated verification value generated by the validation cohort only if the genomic differentiation object assigned to the validation cohort exactly matches the second genomic differentiation object assigned to the creator cohort. In some implementations, the genomic differentiation objects are assigned to the validation cohort by an ecosystem VDAX that controls the genomic topology of the ledger-based ecosystem, and the second genomic differentiation object is assigned to the creator cohort by the ecosystem VDAX.

[0041] In some implementations, the first GRI was generated and provided by the creation cohort during a one-time link exchange process. In some implementations, the first GRI was generated by the validation cohort and provided to the creation cohort during a one-time link exchange process. In some implementations, the first GRI was generated by the enclave VDAX and provided to the creation cohort, the validation cohort, and one or more other validation cohorts, such that the first GRI is used by the validation cohort and one or more other validation cohorts to validate the creator-generated validation value.

[0042] In some embodiments, generating a GEF includes: generating a sequence transformation vector based on the sequence and the first GRI; mapping the modified genome differentiation object based on the sequence transformation vector to obtain a mapped genome differentiation object; and generating a GEF based on the mapped genome differentiation object.

[0043] In some implementations, generating a verifier-generated verification value includes: generating a link value based on the MDB and a preceding MDB in the MDC, where the link value indicates a combination of the MDB and the preceding MDB; and applying one or more computational functions to the link value based on the GEF to obtain a verifier-generated verification value. In some of these implementations, generating a link value includes: generating a first hash value of at least a portion of the MDB; generating a second hash value of at least a portion of the preceding MDB; and combining the first hash value and the second hash value to obtain a link value. In some implementations, the one or more computational functions include one or more cryptographic-based functions that use the GEF as a key to transform the link value into a verifier-generated verification value. In some implementations, the one or more cryptographic-based functions include an encryption function. In some implementations, the one or more cryptographic-based functions include a disambiguation function. In some implementations, the one or more computational functions include one or more cryptographic-less functions that use the GEF as an input parameter to transform the link value into a verifier-generated verification value. In some of these implementations, the one or more cryptography-less functions include a hash function. In some implementations, the one or more computational functions include one or more cryptography-less functions and one or more cryptography-based functions that, in combination, use the GEF as an input parameter to transform the link value into a verifier-generated verification value.

[0044] In some implementations, the method further includes sending the validation record to the creator cohort. In some of these implementations, the method further includes storing, by a processing system of the validation cohort, the validation record in a validation chain maintained by the validation cohort. In some of these implementations, storing the validation record includes: generating a validation block including one or more validation records corresponding to one or more respective MDBs in the MDC; and adding the validation block to the validation chain. In some of these implementations, the validation chain is a side chain of a second MDC maintained by the validation cohort. In some implementations, the creator cohort stores the validation record in a creator cohort validation chain that maintains validation records provided by the validation cohort. In some of these implementations, the creator cohort validation chain is a side chain of the MDC.

[0045] According to some embodiments of the present disclosure, a method for maintaining a material data blockchain (MDC) is disclosed. The method includes receiving, by a first set of processors executing a ledger-based application and a creator VDAX corresponding to a creator cohort, an unnotarized material data block (MDB) from the ledger-based application, the unnotarized MDB including a metadata portion and a payload portion including entity data stored in the MDB. The method further includes extracting, by the creator VDAX, a first sequence of bits from the metadata portion of the MDB, and generating, by the creator VDAX, a first genome engagement factor (GEF) based on the first sequence, a first genome differentiation object assigned to the creator VDAX, and a first genome coordination instruction (GRI) maintained by the creator VDAX. The method further includes applying, by the creator VDAX, one or more computational functions to the unnotarized MDB using the first GEF as an input to obtain a creator-generated value corresponding to the unnotarized MDB. The method also includes updating, by the first set of processors, the unnotarized MBD by digitally signing the unnotarized MBD with the creator-generated value; providing, by the first set of processors, the unnotarized MDB to one or more notary cohorts; and receiving, by the first set of processors, a respective notarization value from each of the one or more notarization cohorts, where each notarization value is generated by each notarization cohort of the one or more notarization cohorts using a respective GRI held by the respective notarization cohort and a respective genome differentiation object assigned to the respective notarization cohort. The method further includes updating, by the first set of processors, the unnotarized MDB with the respective notarization values ​​received from the one or more notarization cohorts to obtain a notarized MDB, and adding, by the first set of processors, the notarized MDB to the MDC.

[0046] In some embodiments, the method further includes: receiving, by a second set of processors executing a notary VDAX corresponding to a notary cohort of the one or more notary cohorts, the unnotarized MDB; extracting, by the notary VDAX, a second sequence of bits from the metadata portion of the MDB; generating, by the notary VDAX, a second genome engagement factor (GEF) based on the second sequence, a second genome differentiation object assigned to the notary VDAX, and a second GRI maintained by the notary VDAX; applying, by the notary VDAX, one or more computational functions to the unnotarized MDB using the second GEF as input to obtain a notary value corresponding to the unnotarized MBD; and providing, by the second set of processors, the notary value to the creator cohort. In some of these implementations, the method further includes digitally signing, by a second set of processors, the unnotarized MDB with the notarization value to obtain an at least partially notarized MDB, and updating, by the second set of processors, a second MDC maintained by the notarization cohort with the at least partially notarized MDB. In some of these implementations, providing the notarization value to the creator cohort includes providing the at least partially notarized MDB to the creator cohort. In some embodiments, generating the second GEF includes determining a sequence transformation vector (SCV) based on the second sequence and the second GRI. In some of these embodiments, generating the second GEF includes: mapping a second genome differentiation object based on the SCV to obtain a mapped genome differentiation object; modifying the mapped genome differentiation object based on at least one of the SCV and the second GRI to obtain a modified mapped genome differentiation object; and determining the second GEF based on the modified mapped genome differentiation object.In some of these embodiments, generating the second GEF includes: modifying the second genome differentiation object based on the second GRI to obtain a modified genome differentiation object; mapping the modified genome differentiation object based on the SCV to obtain a mapped, modified genome differentiation object; and determining the second GEF based on the mapped, modified genome differentiation object. In some of these embodiments, the method further includes storing the modified genome differentiation object such that the modified genome differentiation object is used to determine subsequent GEFs for other MDBs received from the creator cohort. In some embodiments, the second sequence of bits is equal to the first sequence of bits. In other embodiments, the second sequence is different from the first sequence of bits. In some implementations, the one or more computational functions include one or more cryptography-based functions that convert at least a portion of the unnotarized MDB into a notarized value using the second GEF as a key. In some of these implementations, the one or more cryptography-based functions include an encryption function. In some implementations, the one or more cryptography-based functions include a disambiguation function. In some implementations, the one or more computational functions include one or more crypto-less functions that take the second GEF as an input parameter and convert at least a portion of the unnotarized MDB into a notarized value. In some of these implementations, the one or more crypto-less functions include a hash function. In some implementations, the one or more computational functions include one or more crypto-less functions and one or more crypto-based functions that, in combination, use the second GEF as an input parameter to convert at least a portion of the unnotarized MDB into a notarized value. In some embodiments, the second genome differentiation object and the first genome differentiation object are correlated genome differentiation objects. In some embodiments, the second genome differentiation object and the first genome differentiation object are uncorrelated genome differentiation objects.In some embodiments, the method further includes receiving, by a second set of processors, a verification request from the validation cohort, the verification request including the notarized MDB, and the method further includes generating, by the notarizer VDAX, a recreated notarization value based on the notarized MDB based on a third sequence of bits extracted from the notarized MDB, the second GRI, and the second genomic data object. In some embodiments, the method further includes providing, by the second set of processors, a response to the validation cohort indicating the recreated notarization value, wherein in response to determining that the recreated notarization value matches the notarization value used to notarize the notarized MDB, the validation cohort confirms the integrity of the notarized MDB. In some of these embodiments, the method further includes determining, by the second set of processors, whether the recreated notary value matches the notarizer value used to notarize the notarized MDB, and in response to determining that the recreated notarizer value matches the notarizer value used to notarize the notarized MDB, providing, by the second set of processors, a response to the verifying cohort confirming the integrity of the notarized MDB.

[0047] According to some embodiments of the present disclosure, a device is disclosed. The device includes a VDAX having a genomic dataset assigned thereto, the genomic dataset corresponding to a digital ecosystem of which the device is a member, the genomic dataset including a genomic eligibility object, a genomic correlation object, and a genomic differentiation object. The VDAX includes link generation means for generating a first link for a second device in the digital ecosystem, the link including first genomic coordination instructions (GRI) used by the second device to generate a virtual binary language script (VBLS) decodable by the VDAX. The VDAX further includes VBLS decoding means for decoding the VBLS generated by the second device for the device based on the first GRI and the genomic differentiation object to obtain a decoded digital object encoded in the VBLS.

[0048] In some embodiments, the VDAX further includes a link host means for decoding a second link received from the second device to obtain a second GRI generated by the second device, the second link including a second encoded GRI, second encoded link information for decoding the encoded GRI, and a second public sequence used to decode the second encoded link information. The VDAX also includes a VBLS encoding means for encoding a digital object for the second device based on the second GRI and a genome differentiation object. In some embodiments, the link host means includes: means for generating a correlation vector based on the genome eligibility object, a first credential for the device, and a second credential for the second device; means for decoding the second encoded link information based on the correlation vector and obtaining a set of zone references; means for generating a window vector based on the set of zone references and the genome correlation object; means for generating a transformation value based on the public sequence, the window, and the genome correlation object for the device; and means for decoding the second GRI using the transformation value to obtain the second GRI. In some of these embodiments, the VBLS encoding means includes means for generating a genomic engagement factor (GEF) based on the first GRI, the sequence extracted from the unencoded portion of the digital object, and the genomic differentiation object, and means for encoding the digital object based on the GEF to obtain a VBLS object decodable by a second device. In some of these implementations, the means for encoding the digital object encrypts a payload of the digital object using the GEF as a key to obtain the VBLS object. In some implementations, the means for encoding the digital object XORs the payload of the digital object with the GEF to obtain the VBLS object.

[0049] In some embodiments, the first link includes an encoded GRI including the first GRI, encoded link information including information used by the second device to decode the first GRI from the encoded GRI, and a public sequence used by the second device to decode the link information from the encoded link information. In some of these implementations, the link generation means generates the first link based on the genome eligibility object, the genome correlation object, a first credential corresponding to the device, and a second credential corresponding to the second device. In some of these implementations, the link generation means generates the first link by generating a correlation vector based on the genome eligibility object, the first credential, and the second credential assigned to VDAX, and the correlation vector is used to encode the link information. In some of these embodiments, the genome eligibility object is a first CNA object uniquely derived from the master CNA object using the first credential. In some of these embodiments, the link generation means includes means for generating a correlation vector based on the first CNA object, the first credential, and the second credential. In some of these implementations, the means for generating the correlation vector is configured to: apply a code mapping function to the first credential to obtain a first code vector that uniquely corresponds to the first device and indicates a respective portion of the master CNA object assigned to the first CNA object; apply the code mapping function to the second credential to obtain a second code vector that uniquely corresponds to the second device and indicates a respective portion of the master CNA object assigned to a second CNA object assigned to the second device; determine an intersection vector that indicates an intersection of the first code vector and the second code vector; and determine a correlation vector from the first CNA object based on the intersection vector. In some of these implementations, the correlation vector represents a unique correlation between the first CNA object of the device and the second CNA object of the second device.

[0050] In some embodiments, the genome eligible object is a PNA object that is uniquely derived from a master PNA object using a first credential corresponding to the device, and the PNA object includes a secret ratio that is secretly maintained by the device, a public primitive polynomial assigned to the device, and a public component of the master PNA object.

[0051] In some of these implementations, the public component of the master PNA object includes a public component including a first master primitive polynomial of degree M, a second master primitive polynomial of degree M, and (N-1) respective pairs of M-bit values, and the first credential and the second credential are represented by N-bit vectors. In some implementations, the link generation means includes means for generating a correlation vector based on the device's PNA object and engagement information of the second device. In some of these implementations, the engagement information of the second device includes a credential corresponding to the second device and a second public polynomial assigned to the second device.

[0052] In some implementations, the link generation means includes: means for generating a correlation vector based on the genome correlation object, a first credential of the device, and a second credential of the second device; means for generating a first GRI; means for generating a set of zone references, each zone reference indicating a respective portion of the genome correlation object; means for generating a window vector based on the genome correlation object and the set of zone references; means for generating a public sequence based on the window and the first GRI; means for generating a transformation value based on the public sequence, the window, and the genome correlation object of the device; means for encoding the first GRI using the transformation value to obtain an encoded GRI; and means for encoding the set of zone references with the correlation vector to obtain encoded link information. In some of these embodiments, the device provides the encoded link information, the public sequence, and the encoded GRI to a second device, and the second device decodes the encoded GRI based on the encoded link information, the public sequence, a second genome dataset assigned to the second device, the first credential, and the second credential.

[0053] In some embodiments, the means for decoding the VBLS includes: means for generating a genomic engagement factor (GEF) based on the first GRI, the sequence extracted from the unencoded portion of the VBLS object, and the genomic differentiation object; and means for decode the VBLS object based on the GEF to obtain a decoded portion of the digital object. In some of these embodiments, the means for decoding the VBLS object decodes the encoded portion of the VBLS object using the GEF as a key to obtain the decoded portion of the digital object.

[0054] In some implementations, the means for decoding the VBLS object disambiguates the encoded portion of the VBLS object by XORing the unencoded portion of the VBLS object with the GEF to obtain a decoded digital object.

[0055] In some implementations, the digital object is a media frame. In some implementations, the digital object is a network packet. In some implementations, the digital object is a file.

[0056] A more complete understanding of the present disclosure will be realized from the following description and accompanying drawings, and the appended claims. [Brief explanation of the drawings]

[0057] For a better understanding of the present disclosure, the accompanying drawings illustrate embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0058] [Figure 1] FIG. 1 is a schematic diagram illustrating features of a CipherGenics-based digital ecosystem in relation to organic ecosystems and modern digital ecosystems, according to some embodiments of the present disclosure.

[0059] [Figure 2] FIG. 2 illustrates an example of a CipherGenics-enabled security stack that may be applied in parallel with commonly known application and network stacks, according to some embodiments of the present disclosure, and a CipherGenics-facilitated genome architecture of a digital ecosystem that may result from such application.

[0060] [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.

[0061] [Figure 4] FIG. 4 illustrates an example configuration of a CipherGenics-based ecosystem security platform according to some embodiments of the present disclosure.

[0062] [Figure 5] FIG. 5 illustrates an exemplary implementation of a genomic dataset according to some embodiments of the present disclosure.

[0063] [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.

[0064] [Figure 7] FIG. 7 illustrates an example implementation of a security platform instance configured according to a directional architecture that supports a static ecosystem, in accordance with some embodiments of the present disclosure.

[0065] [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.

[0066] [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.

[0067] [Figure 10] FIG. 10 illustrates an example implementation of a security platform instance configured according to a transient architecture that supports virtual trusted execution domains, in accordance with some embodiments of the present disclosure.

[0068] [Figure 11] 11 and 12 show examples of various CG-enabled digital ecosystems that may be formed in accordance with some embodiments of the present disclosure. [Figure 12] 11 and 12 show examples of various CG-enabled digital ecosystems that may be formed in accordance with some embodiments of the present disclosure.

[0069] [Figure 13] FIG. 13 illustrates an exemplary process for generating a genomic data object according to some embodiments of the present disclosure.

[0070] [Figure 14] 14 and 15 illustrate an example process for generating an LNA object according to some embodiments of the present disclosure. [Figure 15] 14 and 15 illustrate an example process for generating an LNA object according to some embodiments of the present disclosure.

[0071] [Figure 16] 16 and 17 illustrate an exemplary process for creating an XNA object according to some embodiments of the present disclosure. [Figure 17] 16 and 17 illustrate an exemplary process for creating an XNA object according to some embodiments of the present disclosure.

[0072] [Figure 18] FIG. 18 illustrates an exemplary process for creating a ZNA object, according to some embodiments of the present disclosure.

[0073] [Figure 19] FIG. 19 illustrates an example process for creating a master CNA object according to some embodiments of the present disclosure.

[0074] [Figure 20] FIG. 20 illustrates an example process for allocating CNA objects for ecosystem members based on a master CNA object, according to some embodiments of the present disclosure.

[0075] [Figure 21] FIG. 21 illustrates an example process for creating and allocating PNA objects for ecosystem members of a digital ecosystem, according to some embodiments of the present disclosure.

[0076] [Figure 22] FIG. 22 illustrates an exemplary process for performing sequence mapping on an unmodified genomic data object, according to some embodiments of the present disclosure.

[0077] [Figure 23] FIG. 23 illustrates an exemplary process for performing sequence mapping on a corrected genomic data object according to some embodiments of the present disclosure.

[0078] [Figure 24] FIG. 24 illustrates an example process for performing a link exchange in accordance with some embodiments of the present disclosure.

[0079] [Figure 25] FIG. 25 illustrates an example process for generating a Virtual Language Binary Script (VBLS) according to some embodiments of the present disclosure.

[0080] [Figure 26] FIG. 26 illustrates an example VDAX configured to decode VBLS-encoded instructions into decoded executable instructions and / or encode executable instructions into VBLS-encoded instructions in accordance with some embodiments of the present disclosure.

[0081] [Figure 27] FIG. 27 illustrates an example VDAX configured to decode VBLS-encoded data into decoded data input to a processing core and / or encode output data output by a processing core into VBLS-encoded data, in accordance with some embodiments of the present disclosure.

[0082] [Figure 28] FIG. 28 illustrates an example configuration of a ledger-based ecosystem and underlying VDAX, according to some embodiments of the present disclosure.

[0083] [Figure 29] FIG. 29 illustrates an example ledger-based digital ecosystem that uses CG-based validation to maintain in-ledger MDC, in accordance with some embodiments of the present disclosure.

[0084] [Figure 30] FIG. 30 illustrates an example ledger-based digital ecosystem that uses CG-based notarization to maintain inter-ledger MDC, in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0085] 〔introduction〕 Hyperscalability (e.g., the ability for N digital cohorts to directly establish mutual identities of interest across N distinct digital cohorts, exhibiting high entropy) is critical to anything network-centric and the missing link in comprehensive security for dynamic state virtualization of digital ecosystems. Hyperscalability (e.g., many-to-many), in addition to comprehensive security, may facilitate entirely new network-centric virtualization products and services that cannot be achieved with typical linear scalability techniques, e.g., public key infrastructure (one-to-many) or quantum key distribution (one-to-one), whether or not they achieve quantum resistance.

[0086] N × N engagement instances X ×N Y While potentially increasing in complexity, CipherGenics-enabled hyperscalability requires minimal overhead or additional bandwidth, as discussed herein. CipherGenics-enabled hyperscalability is equally effective across all network and application stack levels and is computationally agnostic. In embodiments, the results of virtual authentication and virtual affiliation between hyperscalable direct digital cohorts are similar to the results of virtual correlation and virtual differentiation between hyperscalable biological descendants, even if the underlying technologies enabling each are significantly different. The same is true for other digital attributes (e.g., virtual agility, virtual data objects, virtual code objects) enabled by CG-based hyperscalability.

[0087] The discussion of CipherGenics as a fully digitally realized technology will likely be familiar to all but a few at this point, and will be substantially aided by the adoption of a concomitant genomic term, preferably one that is more familiar, fully biochemically realized. While the specific biochemical processes do not technically impact CipherGenics, their ability to address similar challenges and levels of complexity adds a posteriori merit to the fundamental propositions of CipherGenics. Non-limiting examples of CipherGenics-based terminology and computationally complex digital functions and processes, particularly when they share genomic expression, include: Genomic information: Numerical, narrative, or other scripts whose elements as a whole exhibit little computationally discernible order or relationship. Genomic entropy: A computational assessment and confirmation of the degree to which genomic information lacks repeating or predictable patterns. Genome Construction: The ability to rearrange or reconstruct genomic information into specific subsets from the original sequence and relationship base without losing relative entropy. Genome Modification: The ability to manipulate genomic information based on computationally complex functions and processes whose computational properties are provable, if not observable, and consistent across the entire modified genomic information base. Genomic regulation: The ability to conditionally and temporarily alter the complete genomic information base or a specific subset to achieve a specific goal (e.g., precise unadjusted expiration of a digital cohort), requiring prior knowledge of the current (i.e., altered) base at that time. Genome Revision: The ability to derive a subset of genomic information by rearrangement, modification, or adjustment, enabling a unique, computationally proven, non-iterative transformation of digital data objects or digital code objects while preserving specific genomic correlations and differentiating attributes.

[0088] This disclosure relates to various embodiments of the CipherGenix Ecosystem Security Platform (also referred to as the "CG-ESP," "security platform," or "genome security platform") and the processes and technologies it facilitates. In embodiments, the CG-ESP provides computational resources and process control where genomic information, genomic entropy, genomic construction, genomic modification, genomic adjustment, and genomic revision uniquely collaborate to exhibit computationally complex hyper-scalability. In embodiments, hyper-scalability enables digital ecosystems, enclaves, and digital cohorts to engage on a genomic basis to achieve virtual authentication, virtual collaboration, virtual agility, virtual sessionless engagement, and / or virtual execution domain attributes. In some embodiments, these attributes facilitate application-specific genomic network security topologies without replacing or reconfiguring hardware connections.

[0089] 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 of 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 Anonymity Exchange Controller (or “VDAX”), which may be executed by a processing system to enable the VDAX to perform its role within the respective digital community. As described below, a VDAX may be embodied in different ways depending on the type of device in which the VDAX functions, the intended purpose of the VDAX, and / or the desired level of security. For example, in some implementations, a VDAX may be embodied as software, firmware, or other computer-executable instructions executed by a processor. In some implementations, a VDAX may be embodied in hardware, such as a dedicated core of a processing device, a field programmable gate array, a microprocessor, a system on a chip (SoC), and / or the like. In some implementations, a VDAX may be a combination of hardware and software elements. In embodiments, a CG-ESP can enable multiple VDAXs, even if they have mutual identities of disparate or overlapping interest.

[0090] In embodiments, the CG-ESP may be configured to build and manage digital correlation and differentiation capabilities 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 set 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 (including an IoT sensor). A dependent cohort may refer to an individual digital entity realized 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 below, the designation of independent and dependent cohorts can vary among various types of architectures and ecosystems. For example, according to some embodiments of a transient architecture (described below), certain device components (e.g., processors, processor cores, cameras, etc.) and software instances may be designated as independent cohorts, while other device components and software instances may be designated as dependent cohorts. Note that in some embodiments, these types of designations may be determined by a community owner associated with the digital ecosystem.

[0091] 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 subordinate cohorts with mutual identities of interest. In an embodiment, the CG-ESP provides one or more core competencies, such as platform capabilities to control and manage genomic functions and processes, and link exchange capabilities that provide a means by which linked data (e.g., genomic engagement cargo) is exchanged. In an embodiment, mutual identities of interest can be defined according to any logical commonality between cohorts within an enclave, and this commonality can 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, mutual identity 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), such that the home network has one or more enclaves (e.g., a work-related enclave used for a home office and a personal enclave for personal or family devices, applications, and files). In another example, mutual identity of interest may exist among autonomous vehicles (e.g., cohorts) traveling 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 are discussed throughout this document.Additionally, it should be noted that because digital entities may be considered a cohort within a first ecosystem (e.g., mobile devices within an enterprise ecosystem), digital entities may play different roles within an ecosystem or across multiple ecosystems. For example, mobile devices within an enterprise ecosystem may be considered an enterprise ecosystem cohort, but may define an entire digital ecosystem within a viable ecosystem.

[0092] As will be discussed 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, this term may refer to an entity (e.g., a business, organization, government, individual human being, etc.) that manages, maintains, or owns the community and / or its representative (e.g., a network administrator, a CIO, an IT manager, a homeowner, a consultant, a 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 pre-configured and sold to the community owner, which may or may not enable the community owner to make decisions regarding community membership and / or decisions regarding the functionality of the CG-ESP (e.g., which CG-ESP modules and configurations are used in the CG-ESP).

[0093] In the context of biology, core biological genomic capabilities, including biological differentiation and correlation, provide convenient reasoning for describing the formulation of CG-ESP digital processes. However, it is understood that reference to or derivation of "genomic" cohorts (e.g., genomic datasets, DNA, sequence mapping, mutation, cloning, and / or the like) in the context of CipherGenics-related technologies is not intended to imply that these processes mimic or inherent any or all specific characteristics of biological genome construction or processes. In embodiments, CG-ESP performs genomic processes that may include the digital creation, 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 constructions. In some embodiments, these constructions may be referred to as digital DNA (or genomic data). In embodiments, digital DNA may include one or more information-theoretically facilitated constructions, such as LNA (Genome Correlation), CNA (Genome Engagement Integrity), PNA (Genome Engagement Competence), XNA (Genome Differentiation), and / or ZNA (Genome Code Separation / Cloaking). As described below, these cyphergenics-based (or "CG-based") processes and constructions facilitate hyperscalability across a broad digital ecosystem. Examples of CG-based processes include, but are not limited to, CG-based linking processes, CG-based sequence mapping, and / or CG-based transformations, example implementations of which are described throughout this disclosure.

[0094] In embodiments, genomic digital links (or "links") enable the exchange of information necessary for a Virtual Digital Anonymous Exchange Controller ("VDAX") (discussed further below) to perform higher-level, computationally complex genomic functions. In embodiments, CG-based genomic linking processes can include link spawning, link hosting, and / or link updating, examples of which are described throughout this disclosure. These CG-based linking processes provide attribute-specific genome construction information, such as LNA (genomic correlation), CNA (genomic engagement integrity), and PNA (genomic engagement eligibility).

[0095] In embodiments, CG-based sequence mapping may refer to techniques used in the 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-repeating. While different types of sequences may be widely heterogeneous, sequences may be processed in a manner 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 are used to encode the digital object into a VBLS.

[0096] As will be described, embodiments of CG-ESP can 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 (unlimited differences), virtual authentication (unlimited correlations), virtual agility (unlimited structural adaptability), and virtual binary language scripting (VBLS), which enables virtual engagement (object sessionless control per discrete data object) and virtual trusted execution domains (code object execution control per discrete code object). Note that the term "unbounded" as used herein means unbounded in all practical senses, while recognizing that it is theoretically possible to describe "bounded" scenarios.

[0097] Hyperscalability: In some embodiments, hyperscalability may refer to the ability to comprehensively associate N cohorts (or other community members) by M contacts across T instances (M×N×T). Given that there are billions of potential cohorts with countless contacts and communicating on 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 little overhead and / or bandwidth.

[0098] 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 driven genomic processes for a digital community (e.g., cohort, enclave, ecosystem, etc.) to uniquely verify the identity of another member (e.g., another cohort, enclave, ecosystem, etc.). In embodiments, virtual authentication may refer to the ability to authenticate an unlimited number of ecosystem members (e.g., ecosystems, enclaves, cohorts, etc.). As discussed, a CG-enabled ecosystem can achieve unlimited correlation for ecosystem members (e.g., enclaves, cohorts, subordinate cohorts), thereby providing an unlimited amount of unique relationships formed. In some embodiments of the present disclosure, cohorts from different ecosystems may also be configured to authenticate each other in an unlimited manner. As described, unlimited correlation may be achieved through genomic computationally complex constructions and processes (also referred to as "CypherGenics-based" or "CG-based" or "CG-enabled" constructions and / or processes).

[0099] Virtual Alliance: 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 (“what is what” and “only us”) may be required. In embodiments, precise and unique differentiation may refer to a matching or sufficiently matching set of processes performed by a unique pair of community members that establishes a unique engagement that differentiates that pair from other community members. 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 unlimited number of other ecosystem members (e.g., ecosystems, enclaves, cohorts, and / or the like). Organic ecosystems demonstrate bounded yet powerful differentiation between species, offspring, and siblings, resulting from complex biochemical processes. However, unlimited differentiation may be achieved through specific genomic information theory-based digital constructions and processes. As will be discussed, CG-enabled ecosystems offer the potential to achieve unlimited differentiation for ecosystem members (e.g., enclaves, cohorts, subordinate cohorts), resulting in the formation of an unlimited amount of unique relationships. In some embodiments of the present disclosure, cohorts from different ecosystems may also be configured to form unique engagements in an unlimited manner. As will be discussed, unlimited differentiation may be achieved through genomic information theory-governed constructions and processes (also referred to as "cyphergenics-based" or "CG-based" constructions and / or processes).

[0100] 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 hyperscalable correlation for software and hardware management processes. In some embodiments, agile execution of software and / or hardware management 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 constructions and processes driven by specific genomic information theory.

[0101] Virtual Binary Language Script (VBLS): Engagement of Virtual Binary Language Script-enabled ecosystems, enclaves, and / or cohorts requires hyperscalability. Hyperscalability technologies allow ecosystems, enclaves, and cohorts to engage via unique, acyclic, computationally quantum-proof binary languages ​​(or non-quantum-proof binary languages, if desired by the community owner). Organic ecosystems, based on complex biochemical processes, have demonstrated limited but powerful unique cellular engagement. However, unlimited unique digital object engagement may be achieved through digital constructions governed by specific genomic information theory.

[0102] Virtual Trusted Execution Domain: In some embodiments, employing computationally complex genome construction and processes, a properly configured CG-ESP enables a process for uniquely transforming the engagement of components in an executable ecosystem. In embodiments, an executable ecosystem may refer to various software and hardware components of a device (or a system of interdependent devices operating as a single unit). In embodiments, the components of an executable ecosystem may include, for example, but are not limited to, application components (APIs, libraries, threads), operating system components (e.g., kernels, services, drivers, libraries, and the like), 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 execute (e.g., encode and / or decode) executable binaries.

[0103] Genome-Facilitated Virtual Network Architecture: In embodiments, the genome processes and capabilities of CG-EPS enable the inversion of application security and network architecture related protocols without regard to the unique requirements of a particular use case. In some embodiments, the disclosed "genome 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 genome topologies. As used herein, genome topology or genome network topology may refer to the topology of a digital ecosystem, defined using the genome constructions of each member of the digital ecosystem.

[0104] In embodiments, ecosystems, enclaves, and their membership are defined by a digital community owner. For example, a network administrator belonging to a corporate entity may configure a security platform instance that establishes respective enclaves for various 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 cohort's function. 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 discussed, a CG-ESP may be configured to form many different types of ecosystems, and membership may be configurable and definable by the community owner and / or CG-ESP provider. In embodiments, the security platform is configured to "genomically" construct a field of heterogeneous functions, systems, and / or operations that are "genomically" based on the mutual identities of those of interest. Stated another way, in these embodiments, the CG-ESP may be configured and operated to control (e.g., by a community owner or similar party) the genomic network topology of a digital ecosystem using the genomic constructions of community members (e.g., enclaves, ecosystems, and / or cohorts) within the digital community. In this manner, community members may be established, added to, or revoked from a particular enclave or ecosystem by modifying the genomic constructions of one or more members within the digital community.

[0105] 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 that depends on the role the CG-ESP instance performs 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 competencies, which may include control and management of genome construction, functions, and processes (platform competence) and / or secure data exchange functions and processes (link exchange competence). In embodiments, an ecosystem VDAX performs security-related functions on behalf of the ecosystem and may be considered the "progenitor" 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 dependent VDAX may perform genome security-related functions on behalf of each dependent cohort in the ecosystem. Note that in some embodiments, an independent cohort may host one or more dependent VDAXs on behalf of one or more dependent cohorts that depend on the independent cohort.Furthermore, in some embodiments, a single Cohort VDAX associated with an independent cohort may be configured to perform security-related functions for the cohort across heterogeneous enclaves and ecosystems. In these embodiments, the Cohort VDAX can manage and leverage various genomic datasets on behalf of the cohort according to the respective configurations of the various 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, and may also be configured with a Cohort VDAX that manages and leverages 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 hosted on a distributed cloud computing system (e.g., AWS®, Azure®, Google Cloud Services®, privately owned server banks, etc.), while the security controller of the ecosystem and enclave of a smaller digital ecosystem (e.g., home network, small office network, grassroots non-profit organization, 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.

[0106] For ease of explanation, the terms "founder" and "progeny" (e.g., "founder security controller" or "founder VDAX" and "descendant security controller" or "descendant VDAX") are used to indicate a relationship in which a "founder" VDAX generates, assigns, and / or provides genomic datasets to "descendant" VDAXs. For example, in some embodiments, an ecosystem VDAX can modify unique digital genomic datasets 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 "founder" ecosystem VDAX. Similarly, in another example, an enclave VDAX can modify unique genomic datasets for each cohort such that its descendant cohort VDAX is assigned a unique genomic dataset to generate, assign, and / or otherwise provide unique correlated genomic datasets for the cohorts within the enclave. In this way, descendants (e.g., descendant VDAXs) of a founder VDAX may be able to exchange data in a cryptographically secure manner, possibly due to a high degree of correlation between their respective genomic datasets. Note that in some embodiments of the CG-ESP platform, an ecosystem VDAX can generate and allocate genomic datasets for ecosystem cohorts even in the presence of an enclave VDAX. In embodiments, a founder VDAX (e.g., ecosystem or enclave VDAX) can provide 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 below, the configuration can include respective modules configured with specific cypher-based, cypherless, and / or hybrid computationally complex functions used in the CG-based processes discussed. In embodiments, a cypher-based function may refer to an execution function in which all stages of the function (one or more stages) 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+, SAFER++, TOES, etc. In embodiments, a cipher-less function may refer to a function performed in which no stage of the function is performed using a key-based reversible function. In embodiments, a hybrid function may refer to a function performed 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 cipher-less function converts the intermediate value to an output value, which may or may not be reversible.

[0107] In some embodiments, the CG-ESP is configurable by or on behalf of the ecosystem owner. As previously mentioned, the CG-ESP can include a set of interdependent modules that collectively perform one or more genomic security functions, such that any level of VDAX includes instances of some or all of the interdependent modules. Note that these interdependent modules may be implemented as executable instructions executed by conventional processing devices (e.g., CPUs or GPUs and / or FPGAs, microprocessors, or special-purpose chipsets) specifically configured to perform a particular genomic function. In other words, the interdependent modules of a particular security controller instance (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, subordinate VDAX, and / or the like) may be individually embodied as software, middleware, firmware, and / or hardware. References to processor, execution, etc. 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., CNAs, LNAs, XNAs, PNAs, ZNAs, etc.) and / or to perform different types of functions and strategies. For example, some modules may be configured to apply cryptographically-based, computationally complex functions to genomic data objects and / or digital data generated or utilized in connection with genomic security operations. Examples of cryptographically-based, computationally complex functions include, but are not limited to, Advanced Encryption Standard (AES) encryption / decryption, SAFER+ encryption / decryption operations, XOR 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 computationally complex functions to genomic data objects and / or digital data generated or utilized in connection with genome security operations. Examples of cryptography-less computationally complex functions include, but are not limited to, cryptographic hash functions, transforms based on parameterized linear equations, transforms based on multivariate equations, lattice-based transforms, parametrized cyclic shift operations, and / or the like. Additionally or alternatively, in some embodiments, some modules may be configured to apply hybrid (e.g., cryptography-based and cryptography-less) computationally complex functions to genomic data objects and / or digital data generated or utilized in connection with genome security operations. As described, hybrid functions can include combinations of cryptography-based and cryptography-less functions. As discussed, the CG-ESP can be configured (e.g., by or on behalf of the community owner) according to the needs and constraints of the digital community it serves. Note that a function may be called "computationally complex" not necessarily because of the complexity of the function, but because of the computational complexity associated with inverting the function without a key or similar information. For example, an XOR function that takes two input parameters and outputs an output value that represents the XOR of the two input parameters may be called "computationally complex" because determining the input parameters given only the output value of the XOR operation would require much greater computational resources.

[0108] In embodiments, CG-ESP is configured to provide secure end-to-end data exchange between ecosystem members using specific genomic datasets containing one or more digitally generated genomic constructions 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 degree of correlation and differentiability between the ecosystem members' respective genomic datasets using a series of CG-based processes. In embodiments, hyper-scalable genomic correlation can provide the ability to have an unlimited community of genomic descendants that can be directly authenticated as fellow enclaves or ecosystem members without the support of out-of-bound trusted services (e.g., certificate authorities, private key exchanges, etc.). In some embodiments, hyper-scalable differentiation can refer to the ability for two well-related 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 (sometimes called "genomic adjustment instructions" or "GRI") from one VDAX to another that define how to modify the genomic dataset (e.g., XNA or ZNA) of the second VDAX to generate a VBLS that is decodable by the VBLS (assuming the link is securely maintained by the second VDAX). In an embodiment, the second VDAX can "host" a link that indicates a GRI corresponding to the first VDAX, thereby enabling the second VDAX to modify its genomic dataset based on the GRI and generate a VBLS readable by the first cohort based on the modified genomic data and GRI.In embodiments, the second VDAX maps the sequence to the corrected genomic data to obtain a genomic engagement factor, which is used to encode the digital object included in the VBLS object (e.g., using disambiguation and / or encryption techniques). 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) that do not have access to the GRI included in the link information cannot decode the encoded digital object in the VBLS.

[0109] As discussed, 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 computationally complex functions, which may be cryptographically based, cryptographically less, 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), utilizing information-theoretic genomics to enable virtual authentication, virtual scalability, and / or virtual agility. In embodiments, CG components are configured by CG-enabled processes to generate a unique, non-recurring digital language (e.g., VBLS) such that two domain components can construct and engage through the VBLS. In embodiments, the 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, such that the digital objects retain the information genome attributes of their progenitor components (e.g., ecosystems or enclaves). In embodiments, hyperscalability, when exercised at the digital object level, enables agile application of CG-based attributes beyond the component level (e.g., at the format and / or protocol level).

[0110] 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 constructions that exhibit a particular level of entropy, such that the level of entropy is configurable. As previously mentioned, for purposes of explanation, references to and derivations from biological genetic concepts are made. For example, terms such as DNA, "genomic data," "mutation," "genomic construction," "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 implement various aspects of the present disclosure. In embodiments, a genomic dataset may include genomic eligibility objects, genomic correlation objects, and / or genomic differentiation objects, which may be generally referred to as "genomic data objects."

[0111] In embodiments, a genomic eligibility object may refer to a digitally generated mathematical object that allows a pair of cohorts to genomically confirm eligibility for engagement, which may be performed as part of a "trustless" authentication process between two VDAXs. In embodiments, a founder VDAX (e.g., an ecosystem VDAX) can derive descendant genomic eligibility objects for its descendants from its genomic eligibility object ("founder genomic eligibility object") so that each descendant receives a unique but correlated genomic eligibility object. Once the genomic eligibility object is assigned, the descendant VDAX can receive the genomic eligibility object. In some embodiments, the descendant VDAX can receive genomic eligibility via a one-time trusted event (e.g., upon ecosystem initiation into a particular ecosystem, when a device is manufactured, configured, or sold, etc.). After this single trusted event, sufficient VDAXs can independently confirm eligibility for engagement 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 that exhibit configurable entropy levels, correlations, and differentiations, which are described in further detail below.

[0112] 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 well-distinguished from other well-correlated VDAXs within the digital community. In embodiments, a genomic correlation object is used by a VDAX to identify link exchange correlations that allow 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 objects of community members of a digital ecosystem are LNA objects or any other suitable type of mathematical object that exhibits configurable entropy and correlation, which are described in further detail below.

[0113] In embodiments, a genomic 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 each other VDAX when the VDAXs successfully host links generated by the other VDAXs. In some embodiments, a first VDAX generates a VBLS for a second VDAX in part by modifying its genomic differentiation object in a manner defined in instructions contained in a hosted link corresponding to the second VDAX, and decodes a VBLS from the second VDAX in part by modifying its genomic differentiation object in a manner defined in instructions contained in a link hosted by the second VDAX with respect to the first VDAX. Examples of genomic differentiation objects include, but are not limited to, XNA objects, ZNA objects, or other suitable types of mathematical objects that exhibit configurable entropy and correlation, which are described in more detail below.

[0114] As described below, various combinations and configurations of CG-ESP modules and genomic datasets can be used in various 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 network-, software-, or hardware-centric. Examples of various architectures include, but are not limited to, directed architectures that can be deployed for static ecosystems (e.g., large enterprises), free-form architectures that can be deployed for transient ecosystems (e.g., social networks, websites), spontaneous ecosystems that can be implemented for dynamic ecosystems (e.g., city-wide autonomous vehicle control systems), ephemeral architectures that can be implemented for executable ecosystems (e.g., operating systems, browsers), and / or Interledger architectures that can be implemented for affirmation ecosystems (e.g., blockchains or other distributed ledgers). In embodiments, these architectures overlaying existing physical network topologies evidence genome-built topologies, and multiple genome-built topologies may exist simultaneously and interoperably. Examples of various architectures and CG-enabled ecosystems are discussed throughout this disclosure. [CipherGenix Ecosystem Security Platform]

[0115] 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, and is configured 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 (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) with different roles, which may execute some, all, or none of the CG processes and computational methods defined in the CG-ESP's respective CG modules. In this way, all "community" members can participate in a CG-enabled digital ecosystem, with corresponding CG-ESP instances executed by and / or on behalf of the community member (e.g., for a subordinate cohort), 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 acting as an ecosystem founder (e.g., ecosystem VDAX) may be configured with a CG-ESP instance that includes CG modules that define CG functions and associated computational methods for generating a genomic dataset (digital DNA set) having one or more specific types of genomic data (e.g., CNA, PNA, LNA, XNA, and / or ZNA), while a community member that is an independent cohort within the ecosystem (e.g., principal VDAX) may be configured with a CG-ESP instance that includes CG modules that define CG processes and computational methods for mutation, link exchange, sequence mapping, digital object transformation, etc. of its genomic data.In this manner, different community members of a CG-enabled ecosystem may execute different instances of a particular CG-ESP 400. Note that in some embodiments, the modules of an instance of CG-ESP 400 are executed by VDAXs of the digital ecosystem that the CG-ESP instance supports. Furthermore, because different types of VDAXs within a particular digital ecosystem may perform different roles within the digital ecosystem, 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 execute 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 perform different respective roles within the digital ecosystem, all VDAXs configured to perform a particular CG process with respect to the digital ecosystem (e.g., XNA and LNA modification, link exchange processing, VBLS generation / decoding, and / or the like) are configured with a CG-ESP module that includes 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, and the like). In this manner, sufficiently related VDAXs can perform particular CG operations in a functionally consistent manner, which allows sufficiently related VDAXs to, for example, verify the eligibility and / or integrity of engagements, spawn and host links, and / or generate and decode VBLS objects.

[0116] 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 subordinate VDAX(s)). In some of these embodiments, each class of VDAX can 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 cryptographic-based and cryptographic-less functionality).

[0117] 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 a virtual machine or a container (e.g., a Docker container). [Root DNA module]

[0118] In embodiments, CG-ESP 400 includes a root DNA module 410. In embodiments, root DNA module 410 manages ecosystem-specific data and genome processes, from which root DNA module 410 forms genome constructions (e.g., DNA sets) that enable specific, highly rigorous differentiation and correlation. In some embodiments, root DNA module 410 can include a CNA module 412, a PNA module 414, an LNA module 416, and / or an XNA module 418.

[0119] 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, eligibility correlation of CG-enabled ecosystem components is enabled by the CG genome processes that form and construct CNA objects. In embodiments, the CNA module 412 can 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, CNAs may enable VDAXs of the same ecosystem to confirm eligibility for engagement. In embodiments, CNAs enable ecosystem VDAXs and sub-ecosystem VDAXs to maintain unique confirmation of eligibility for engagement. In embodiments, the CNA module 412 may be configured to perform genome-based eligibility correlations using extensive information-theoretically driven computationally complex functions. In embodiments, these information-theoretically driven functions may be cryptographically based, cryptographically less, or hybrid computationally complex functions. Non-limiting examples of CNA generation and CNA-based eligibility correlations are provided later in this disclosure.

[0120] 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, the CG genome process that formulates and builds PNA objects enables eligibility synchronization of CG-enabled ecosystem components. 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 ecosystem VDAX and descendant VDAX sub-ecosystems 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 calculated according to computationally complex functions driven by extensive information theory. In embodiments, these information-theoretically driven functions may include cryptography-based, cryptography-less, or hybrid computationally complex functions. Non-limiting examples of PNA generation and PNA-based eligibility correlation are provided later in this disclosure.

[0121] In embodiments, the root DNA module 410 manages ecosystem-specific data and CG genome processes, from which the root DNA module 410 forms LNAs that enable specific and highly rigorous differentiation and correlation. In some embodiments, link exchange correlation of CG ecosystem components is enabled by the CG genome processes that form and construct LNA objects. In embodiments, the LNA module 416 defines CG processes and associated computational methods for establishing specific relationships between individual ecosystem components. In this way, the LNAs can enable specific VDAXs (e.g., members of the same enclave) within the ecosystem to verify link exchange correlation. In embodiments, the LNAs enable VDAXs within the 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, CG-based LNA-enabled link exchanges 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., disymmetric). In embodiments, the LNA root module 416 performs genome-based link exchange correlation, which may be computed according to a wide range of information theory driven computationally complex functions. In embodiments, these information theory driven functions may be cryptography-based, cryptography-less, or hybrid computationally complex functions. Non-limiting examples of LNA generation and LNA-based link exchange are provided later in this disclosure.

[0122] In embodiments, the root DNA module 410 manages ecosystem-specific data and CG genome processes, from which the root DNA module 410 creates XNAs that enable specific, highly rigorous differentiation. In these embodiments, the CG genome processes that create and construct XNA objects may enable engagement differentiation of ecosystem members. In embodiments, the root XNA module 418 uses XNA-specific CG processes and associated computational methods to establish specific relationships between individual ecosystem components. In embodiments, the XNAs enable VDAXs of the same ecosystem to ascertain engagement differentiation. In some embodiments, the XNAs enable VDAXs of different ecosystems (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to ascertain engagement differentiation. Engagement differentiation allows pairs of VDAXs to be sufficiently differentiated from other sufficiently correlated VDAXs for the purposes of securely exchanging data, thereby offsetting the corresponding computational complexity of engagement. In some embodiments, XNA-enabled engagement can be premised on 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 genome-based engagement differentiation, which can be computed according to a broad range of information-theoretically driven computationally complex functions. In embodiments, the information-theoretically driven functions can be cryptography-based, cryptography-less, or hybrid computationally complex functions. Non-limiting examples of XNA generation and XNA-based genome differentiation are provided later in this disclosure.

[0123] 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 creates constructions that enable specific, highly rigorous differentiation, referred to as ZNAs. In embodiments, engagement differentiation of ecosystem EICs is enabled by CG genome processes that create and construct ZNA objects. In embodiments, ZNAs enable VDAXs in the same ecosystem to directly control genome-enabled differentiation processes without participation by other VDAX components. For example, in embodiments, an EIC VDAX (e.g., cores and memory) may 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 CG processes for performing genome-based engagement differentiation, which may be computed according to a broad range of information-theoretically driven, computationally complex functions. In embodiments, the information-theoretically driven functions may be cryptographically based, cryptographically less, or hybrid computationally complex functions. Non-limiting examples of ZNA production and ZNA-based genome differentiation are provided later in this disclosure. [Link Module]

[0124] 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 two-way symmetric engagement. In some embodiments, the link exchange exhibits the same level of entropy as two-way symmetric 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, host links). In embodiments, the link module 430 may be configured to verify engagement eligibility with another VDAX based on its genomic 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 link exchange correlation with another VDAX based on the first VDAX's genomic correlation object. In some embodiments, a link module 430 instance generates a link for 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 that VDAX. In embodiments, the link module 430 instance of a VDAX (e.g., a first VDAX) may host a link using the first VDAX's genomic correlation object by, in part, deciphering information for engaging with the other VDAX from links provided by or on behalf of the other VDAX.It should be noted that various configurations of the link module 430 can utilize various CG genome 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, meaning 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., bi-symmetric). In embodiments, the link module 430 can define a CG process that facilitates genome-based information exchange, where the CG process is computed according to a wide range of computationally complex functions. In embodiments, the computational function may be cryptographically based, cryptographically less, or a hybrid function.

[0125] As previously mentioned, a link may include information that enables symmetric 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, such that when 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 object (e.g., a modified XNA or a modified ZNA). As used herein, "highly correlated" when used in relation to genomic objects may refer to identical and / or otherwise sufficiently correlated genomic objects, such that two genomic objects are said to be "sufficiently correlated" if the degree of correlation between two or more genomic objects allows for the intended CG operation or process to be successfully performed. In embodiments, the GRI may include additional information, such as instructions and / or data used by VDAX during sequence mapping. As described in more detail, such deterministic correction allows two cohorts to be distinguished from all other cohorts to effect the generation of a 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 with a link-specific engagement coefficient to obtain an encoded GRI.The link module 430 can generate a genomic engagement cargo (GEC) that includes the encoded GRI and additional information that the link host VDAX uses to decode the GRI from the GEC based on the information and the genomic data of the link host 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 transformation value based on the information included in the GEC and its genomic dataset, and decodes the encoded GRI using the transformation value to obtain the GRI. The decoded GRI is used by the link host VDAX in generating a VBLS for the link generating VDAX that provided the link.

[0126] In embodiments, the link module 430 may be further configured to update a link. Link updating may refer to a process in which the genomic coordination instructions (GRI) provided from a first VDAX to a second VDAX for a particular engagement between a pair of VDAXs are modified. A link may be updated for any number of reasons, including concerns that the link has been compromised and / or to comply with routine security protocols (e.g., the link is updated daily, weekly, monthly, or in response to a cohort request to update the link). In some embodiments, the link module 430 may update a link by generating link update information, whereby the link update information is provided from the VDAX that generated 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 update information may be a value used to transform the GRI, such that the host VDAX applies that value to the current GRI using one or more computationally complex functions (e.g., cryptography-based, cryptography-less, or hybrid functions) to obtain an updated GRI. In some embodiments, link updates differ from link exchanges in that link update information can be encoded in VBLS, as opposed to link exchanges, which may involve more computationally intensive operations. Thus, link exchanges are performed as a one-time operation, while link updates may be performed any number of times and / or for any suitable reason.

[0127] In embodiments, the link is a static link or a dynamic link. In embodiments, a dynamic link may refer to a link that includes additional information that further differentiates the 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 the pair of VDAXs, but only in connection with 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 for each engagement. In this manner, when a pair of VDAXs exchange dynamic links, the pair of cohorts may 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 a particular engagement.

[0128] In embodiments, the static link module 432 defines CG processes that enable two VDAXs (e.g., a first VDAX and a second VDAX) to securely exchange information (e.g., spawn link and host link) necessary to enable a unique, symmetric engagement, where this exchange exhibits the same level of entropy. In embodiments, the rules and processes governing static linking are defined by the highest-class VDAX in the ecosystem (e.g., the 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, VDAXs may perform link exchanges asynchronously, in that the order of the exchange 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 static link module 432 instance performs genome-based engagement differentiation, which may be computed according to a wide range of computationally complex functions, which may be cryptographically-based, cryptographically-less, or hybrid computationally complex functions.

[0129] In embodiments, the dynamic linking module 434 defines a CG process that allows two VDAXs (e.g., a first VDAX and a second VDAX) to securely exchange the information (e.g., spawn link and host link) necessary to enable a unique, bi-symmetric engagement, whereby the exchange exhibits the same level of entropy. In embodiments, the rules and processes governing dynamic linking 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.

[0130] In embodiments, the dynamic link module 434 can 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, a pair of VDAs that have exchanged dynamic links can modify the CG processes that run for that particular engagement, which can provide an additional layer of security. In some embodiments, the dynamic link module 434 can include an interpreter or just-in-time compiler that processes the instruction sets included in the dynamic link, and the processed instruction sets are executed for a particular engagement, overriding one or more CG processes that run during that engagement. In some embodiments, a first dynamic link module 434 instance can 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, the respective dynamic link modules 434 together use the overwrite CG process(es) for that particular engagement. The second VDAX may use the overwrite CG process(es) to generate the VBLS, and the first VDAX may use the overwrite CG process(es) to decode the VBLS. It will be understood that the reverse data exchange using the second dynamic link, from the second dynamic link module 434 instance to the first dynamic link module 434 instance, operates similarly in that the first VDAX generates the second VBLS using the overwrite CG process(es) defined in the second dynamic link, and the second VDAX decodes the second VBLS using the overwrite CG process(es).

[0131] In an embodiment, a dynamic linking module 434 instance can establish instructions and associated CG process processes that are not shared by other VDAXs, which can be governed by a wide range of options, circumstances, conditions, and objectives. In an embodiment, dynamic linking provides an added level of security because the CG process itself is modified in a unique way for a unique pair of VDAXs.

[0132] In embodiments, the dynamic link module 434 may perform dynamic link exchanges asynchronously, in that the order of the exchange 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., bi-symmetric). In embodiments, the dynamic link module 434 instance performs genome-based engagement differentiation, which may be computed according to a wide range of computationally complex functions, which may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions. [Sequence Mapping Module]

[0133] In embodiments, the sequence mapping module 440 can define a set of CG processes and computational methods for performing sequence mapping. In some embodiments, sequence mapping can be a non-trivial computation for transforming unique, non-repeated digital objects. In embodiments, a sequence mapping module 440 instance can 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) genomic 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 genomic data to obtain results (e.g., genomic engagement factors) that exhibit a particular level of entropy. In embodiments, the sequence mapping module 440 can include a public sequence mapping module 442 and / or a private sequence mapping module 444.

[0134] 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., metadata dependent on a particular protocol or format). In some embodiments, a public sequence mapping module 442 instance may process a given public sequence ("PBS") to derive a specific value (e.g., a sequence transformation vector or "SCV"). In some embodiments, the public sequence mapping module 442 derives the SCV based on a GRI and / or one or more computationally complex functions. In embodiments, the SCV may be processed with a genomic data object (e.g., XNA, ZNA, or LNA) and a GRI to generate a unique vector exhibiting a specific entropy (e.g., a genomic engagement factor or transformation value). In embodiments, the resulting vector may exhibit a level of entropy significantly exceeding 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 features present in unrelated protocols and formats. In embodiments, the public sequence mapping module 442 performs computational genomic processes according to computationally complex functions to generate unique vectors based on the public sequences and genomic differentiation objects (e.g., modified XNA objects). In embodiments, these functions can be cryptographic-based, cryptographic-less, or hybrid computationally complex functions.

[0135] In embodiments, the private sequence mapping module 444 may define a CG-enabled process and associated methods configured to select specific sequences from private sources (e.g., private and / or proprietary protocol or format-dependent metadata) and derive unique vectors exhibiting specific entropy. In some implementations, a private sequence mapping module 444 instance may process a given private sequence ("PVS1") to derive a specific value (e.g., a sequence transformation vector (SCV)) based on a GRI. In embodiments, the SCV may be processed with a genomic data object (e.g., XNA, ZNA, or LNA) and a GRI to generate a unique vector exhibiting specific entropy (e.g., a genomic engagement factor or transformation value). In embodiments, the resulting vector may exhibit a level of entropy significantly exceeding the size of the public sequence used to derive the vector. In embodiments, the private sequence mapping module 444 instance generates unique vectors that can leverage specific features present in unrelated private protocols and formats. In embodiments, the private sequence mapping module 444 instance performs computational genomic processes according to a set of computationally complex functions to generate unique vectors based on the public sequences and genomic differentiation objects (e.g., modified XNA objects). In embodiments, these functions may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions.

[0136] An example configuration of the sequence mapping module 440 is described in more detail below. [Binary conversion module]

[0137] 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 through various computational methods (e.g., disambiguation methods and / or encryption methods). In embodiments, the binary conversion module 450 instance is configured to encode the digital object based on a value (e.g., a genome engagement factor) determined by the corresponding sequence mapping module 440, generating an encoded digital object that may be unique, irreproducible, and / or computationally quantum proof. In embodiments, the binary conversion module 450 instance may further be configured to decode the encoded digital object 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.

[0138] In embodiments, the disambiguation module 452 may define a CG process and computational method for performing a binary transformation of a digital object according to a genomically obtained genomic engagement factor generated by a corresponding sequence mapping module 440 instance, such that the resulting encoded digital object is only susceptible to brute force attacks. In embodiments, the disambiguation module 452 instance may transform the digital object based on the genomic engagement factor by performing an XOR operation between the genomic 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 genomic engagement factor for each digital object, since if the same genomic engagement factor is used to encode two or more digital objects, the disambiguation technique may become vulnerable to more efficient attacks. In embodiments, the disambiguation module 452 instance may be configured to decode the encoded digital object using an inverse disambiguation function and the genomic engagement factor. Assuming the genome engagement factor matches the genome engagement factor used to encode the digital object, the inverse disambiguation function, given the genome engagement factor and the encoded digital object, outputs a decoded digital object. In embodiments, the disambiguation module 452 instance performs genome processing according to computationally complex functions. In embodiments, these functions may be cryptography-based, cryptography-less, or hybrid computationally complex functions.

[0139] 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 genomically obtained genomic engagement factor generated by the corresponding sequence mapping module 440 instance, such 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 genomic engagement factor using any suitable encryption function that receives the genomic 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 genomic engagement factors for each digital object, or may use the same transformation for two or more different digital objects.

[0140] 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 computationally complex functions. In embodiments, these functions may be cryptography-based, cryptography-less, or hybrid computationally complex functions. [Authentication Module]

[0141] In embodiments, authentication module 460 can define CG processes and computational methods configured to authenticate VDAXs with a common genome construction. As discussed, a digital ecosystem built by a top-level VDAX (e.g., an ecosystem VDAX) has a particular distribution of genome data (e.g., CNAs, PNAs, LNAs, XNAs, and / or ZNAs) and further has particular genome eligibility correlation, eligibility synchronization link exchange correlation, and / or engagement correlation attributes. In embodiments, authentication module 460 instances may be configured to enable a corresponding VDAX to verify the engagement correlation of any other VDAXs with a common construction (e.g., related genome data), regardless of their primary genome construction (e.g., members of different enclaves within the digital ecosystem). In an embodiment, authentication module 460 may include a cohort-to-cohort module 462 that defines a CG process and associated computational methodology that allows corresponding VDAXs to verify engagement correlation with another VDAX from the same CG-enabled digital ecosystem based on their common genome construction, regardless of which enclave the VDAX(s) belong to. In an embodiment, authentication module 460 instances are configured to perform secure genome-based engagement correlation of genomic datasets according to a computationally complex function, which may be cryptography-based, cryptography-less, or a hybrid computationally complex function. [Master Consistency Controller]

[0142] As discussed, the conformance of the root DNA construction and supporting genomic processes (e.g., link generation, engagement correlation, VBLS generation, etc.) are 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 manages the conformance of modules on behalf of VDAX. In embodiments, the master integrity controller 470 may include a CG process and associated computational methods that ensure the veracity of VDAX operational performance and configuration management across the digital ecosystem. In embodiments, the master integrity controller 470 may include a genomic process controller 472, an authorization module 474, and an engagement instance module 476.

[0143] In embodiments, the engagement of a VDAX, its genomic 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 a corresponding VDAX). In embodiments, the master integrity controller 470 instance utilizes computationally complex functions to engage with specific modules (e.g., 1 to N). In some of these embodiments, the master integrity controller 470 instance has a respective genomic data set (e.g., CNAs, PNAs, LNAs, and / or XNAs) as well as the modules and may use computationally complex functions to engage with and manage specific modules. In some embodiments, the genomic process controller 472 may use its genomic data and computationally complex functions to verify the integrity of modules and authenticate modules. In these embodiments, the genomic 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 particular operational processes and functions under the control of particular modules to the correct application of 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 a module instance (e.g., processes connecting modules for various CG-based functions).

[0144] 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 correct a particular CG-ESP configuration. In embodiments, the master integrity controller 470 may include a genomic process controller 472, an authorization module 474, and an engagement instance module 476. In embodiments, the genomic process controller module 472 instance performs secure genomic-based validation, disqualification, and correction of VDAX modules and particular VDAX configurations, which may be computed according to a wide range of computationally complex functions. In embodiments, these functions may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions.

[0145] 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 affected 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 such that a single ecosystem or enclave VDAX (e.g., founder) can determine the operational configuration (e.g., master consistency controller 470 intercommunication) of other VDAXs. In embodiments, a founder (e.g., ecosystem VDAX or enclave VDAX) can directly confirm or disqualify the operational status of other VDAXs based on the configuration of the other VDAXs. In embodiments, a progenitor (e.g., an ecosystem VDAX or an enclave VDAX) may have unique genomic characteristics configured and enabled so that updates to authorized modules of the VDAX can be performed in coordination with other authorized CG-ESP modules. In embodiments, a master authorization module 474 instance performs secure genomic-based validation, disqualification, and correction of VDAX modules and specific VDAX configurations, which may be computed according to a wide range of computationally complex functions, which may be cryptographically based, cryptographically less, or hybrid computationally complex functions.

[0146] In embodiments, engagement between two or more VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate 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 the hierarchical genomic relationships exhibited by a particular digital ecosystem community. In embodiments, security instances aggregated at a lower level may be passed on to the next aggregation point or any other higher aggregation point (e.g., from a cohort VDAX to an enclave VDAX), and may be passed on (e.g., from a cohort VDAX and an enclave VDAX to an 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 that govern their primary security instances. 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 dictate how security instances are defined. In embodiments, these definitions may impose certain 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 generated according to engagement account policies. In embodiments, these policies dictate how security instances are accumulated. In embodiments, such accumulation may impose certain computationally complex security functions.In embodiments, the master engagement instance module 476 instance enables VDAXs with a common construction (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to report security instances to other VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) according to a set of engagement reporting policies. In embodiments, these policies dictate how, how often, and to whom security instances are reported. In embodiments, such reporting imposes certain computationally complex security functions. In some embodiments, a VDAX (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, and / or a subordinate VDAX) may be uniquely configured and enabled such that a single VDAX (e.g., an ecosystem VDAX) may define the engagement tracking policy, engagement account policy, and / or engagement reporting policy for a digital ecosystem (e.g., a community). In some embodiments, a single engagement instance module 476 instance may execute multiple tracking policies, account policies, and / or reporting policies using specific computationally complex genomic functions.

[0147] In embodiments, the master engagement instance module 474 may enable VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) that have a common construction (e.g., ecosystem VDAXs) to aggregate security instances from other VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) that are enabled by specific computationally complex genomic functions according to engagement reporting policies. In embodiments, the master engagement instance module 474 performs secure genome-based tracking, calculation, reporting, and aggregation of VDAX genome-specific security instances, which may be computed according to a wide range of cryptographically complex functions, which may be cryptographically based, cryptographically less, or hybrid functions. [Master Verification Module]

[0148] In some embodiments of the present disclosure, the CG-ESP can include a master corroboration module (MCM). As will be described, the master corroboration module may be configured to facilitate a ledger-based architecture. In embodiments, API or VDAX-specified material data may include a wide range of data with specific uses. Such data may be managed according to a specific format, referred to herein as blocks. These MDBs are responsible for computationally complex encoding according to the genomic relationships of the participating VDAXs. In embodiments, the VDAX-resident MCM can aggregate the genomically encoded blocks into dedicated, decentralized, or interacting chains that reflect multiple genomic communities (e.g., which may include multiple VDAXs).

[0149] In embodiments, MDBs transformed according to a particular VDAX-controlled LNA and DNA have highly scalable, relational uniqueness. MDB chains (MDCs) can be aggregated to form computationally related sequences of blocks (e.g., intra-ledger blockchains (ILBs) and inter-ledger blockchains (XLBs)). MDCs are much more efficient and less complex to manage than distributed ledger-based blockchain technologies, for example, allowing cohorts to be established deep within a supply chain ("deep chain") with little significant impact on complexity or efficiency.

[0150] In embodiments, an MDC does not require the creation or use of any form of distributed ledger (DL). In some embodiments, an MDC does not require any form of asymmetrically facilitated consensus. In embodiments, an MDC does not require any form of in-band or out-of-band trusted third-party facilitation. In embodiments, an MDB can support all known protocols and formats, or can be protocol and format agnostic. In embodiments, MDBs can have the same genome encoding or different genome encodings while preserving the mutual identities of interest. In embodiments, a master match module enables MDBs to aggregate into a private chain, thereby blocking the mutual identities of interest and genome correlations. In embodiments, a master match module enables MDBs to form chains with mutual identities of interest that are distributed (e.g., stored) across multiple VDAXs but preserve the genome correlations. In embodiments, a master match module enables MDBs to form direct or decentralized chains that are interactive in that they attest to different genome correlations, but may preserve the functional mutual identities of interest. In an embodiment, the master verification module performs genome-based secure MDC generation, which can be computed according to a wide range of computationally complex functions, which can be cryptographic-based, cryptographic-less, or hybrid functions.

[0151] In embodiments, the CG-ESP may include an Intra-Ledger Function (ILF). In some of these embodiments, a Master Verification Module (MCM) supports the formation of Intra-Ledger Block Computations (MDBs) and MDB Chains (MDCs). In these embodiments, a VDAX may have operational ILF capabilities. In some embodiments, ILF-enabled MDBs may be aggregated as a dedicated chain that blocks shear cross-identities and genomic correlations of interest. In embodiments, ILF-enabled MDBs may form a chain with cross-identities of interest that preserves genomic correlations while being distributed (e.g., stored) across multiple VDAXs.

[0152] In some embodiments, the CG-ESP may include an inter-ledger functionality (ILX). In these embodiments, the master reconciliation module may support the formation of inter-ledger block computations (MDBs) and MDB chains (MDCs). In these embodiments, a VDAX may possess operational ILX capabilities. In these embodiments, ILX-enabled MDBs may be aggregated as a dedicated chain that blocks cross-identities and genomic correlations of interest. In embodiments, ILX-enabled MDBs can form a chain with cross-identities of interest that preserves genomic correlations while being distributed (e.g., stored) across multiple VDAXs. In embodiments, an ILX enables MDBs to form direct or distributed chains that are interactive in that they may exhibit different genomic correlations but preserve the functional cross-identities of interest.

[0153] In embodiments, an MDC may include a set of computational attributes. The rigor of both ILB and XLB hinges on specific computationally complex genomic functions. In some embodiments, an MDC does not have asymmetric computation-based processes or associated attributes. In some embodiments, an MDC-enabled chain may be non-repudiable within a VDAX community with common genomic correlations and differentiations, and is dimensioned VDAX(N) per VDAX(M). In embodiments, the computational infrastructure of an MDC exhibits long-term protection against computationally advanced technologies, such as quantum computing, where the protocol is completely application-agnostic. In embodiments, an MDC can be computationally quantum-proofed without additional bandwidth. In embodiments, an ILX-enabled MDB can be aggregated as a dedicated chain for blocking cross-identities and genomic correlations of interest. The master verification module, ledger-based architecture, and MDC are described in further detail below.

[0154] It should be 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 discussed, different CG-ESPs may be configured to perform different CG operations on different configurations of genomic datasets. Examples of genomic datasets and various CG operations performed on genomic data are discussed in more detail below. [Genomic Data Object]

[0155] FIG. 5 illustrates an example of a genomic dataset 300 (also referred to as a "digital DNA set," "DNA set," or "DNA"). As discussed, in an embodiment, 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 various types of genomic data that may be implemented in conjunction with various CG-ESPs. It will be understood that other types of genomic data may be subsequently developed.

[0156] In embodiments, a DNA set 300 used in connection with a 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") exhibiting configurable entropy. In some embodiments, the digitally generated mathematical objects of a DNA set may include any suitable combination of genomic eligibility objects 310, genomic correlation objects 320, and / or genomic differentiation objects 330. As discussed, various implementations of each CG-ESP may utilize and support different combinations, types, and sizes of genomic data objects, depending on the goals of the respective community owners and / or the type of ecosystem each platform instance supports. Examples of various goals 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, a particular controlled ecosystem (e.g., some executable ecosystems) may only require that certain cohorts (e.g., dependent cohorts of applications, sensors, device drivers, processors, memory devices, etc.) establish a very limited number of relationships (e.g., via links). In such a scenario, the links for each relationship in the ecosystem may be generated at the time of creation of the ecosystem, such that each VDAX has access to every link it needs. In such a scenario, a DNA set may not derive additional benefit from having a particular type of DNA object, and a DNA set in such an ecosystem may be configured without genome eligibility objects 310 or genome correlation objects 320, but may include genome differentiation objects 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 genomic 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 selected and / or the size of the data structure. For example, a genomic differentiation object 330 implemented as a 512 x 512 bit binary vector or bit matrix may provide quantum proof-level security.

[0157] In embodiments of the present disclosure, a genome 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 the two cohorts. In embodiments, a founder VDAX (e.g., an ecosystem VDAX or an enclave VDAX) may derive descendant genome eligibility objects 310 for descendant VDAXs participating in their respective digital ecosystems based on its genome eligibility object (“founder genome eligibility object”). In these embodiments, each descendant VDAX may receive a unique but correlated derivation of the founder genome eligibility object. Furthermore, in some implementations, all genome eligibility objects in an ecosystem may be derived from the founder genome eligibility object so that any ecosystem member can confirm some relationship with another ecosystem member based on their correlated genome eligibility objects (e.g., intersections or shared portions of the founder genome eligibility objects). Once a genome eligibility object 310 is assigned for a particular community, the descendant VDAXs may receive that genome eligibility object. In some embodiments, a descendant VDAX may receive a genome eligibility object in its genome dataset via a one-time trusted event (e.g., upon joining a particular enclave, upon device manufacture, configuration, or sale, etc.). After receiving each genome eligibility object 310, the VDAX may use each genome eligibility object 310 to independently confirm engagement eligibility with one other VDAX in their enclave and / or ecosystem. In embodiments, the genome eligibility 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 construction that allows two community members to confirm engagement eligibility and / or engagement consistency.

[0158] In embodiments, a CNA may refer to a genomic mathematical construction 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 proven. In embodiments, the ecosystem correlation performed by a VDAX is based on a common, computationally complex genomic function, which 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 ascertain the state of that ecosystem without prior knowledge of the other or consultation with a trusted third party.

[0159] 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 configurable, for example, by a community owner. In some embodiments, the configurable entropy level of the CNA object 312 may be an entropy level that is substantially quantum-proof. For example, a substantially quantum-proof CNA object may be configured to exhibit an entropy level 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 in some exemplary embodiments, a quantum-proof CNA object 312 may exhibit less entropy. It is understood that a CNA object 312 that exhibits 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 use CNA objects (or other genomic datasets) that exhibit lower levels of entropy, requiring fewer storage and processing demands, at the expense of overall security. In embodiments, CNA 312 may be configured to establish specific relationships between individual ecosystem members and verify eligibility for engagement using a series of genomic processes and associated computational methods.

[0160] In embodiments, CNA generation for genome 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 genome eligibility correlation applications can be enabled by a high-quality random process with controllable entropy. In embodiments, CNA generation for genome eligibility correlation applications can be enabled based on specific mathematical principles with controllable entropy. In embodiments, CNAs can be generated according to a wide range of information-theoretic complex functions. In embodiments, these information-theoretic functions can be cryptographic-based, cryptographic-less, or hybrid computationally complex functions. Exemplary techniques for generating CNA objects and verifying engagement eligibility are described in more detail throughout this disclosure.

[0161] In embodiments, 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 is based on a general, 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 or consultation with a trusted third party.

[0162] In embodiments, the PNA object 314 may include a set of binary primitive polynomials, as well as other data used during eligibility synchronization. In embodiments, the PNA object 314 is configured to exhibit a particular entropy. In some embodiments, the entropy of the ecosystem's PNA objects 314 is controllable, whereby the entropy may be configurable, for example, by a community owner. In some implementations, the configurable level of entropy of the PNA object 314 may be a substantially quantum-proof level of entropy. For example, a substantially quantum-proof PNA object 314 may be configured to exhibit an entropy level of 256 bits or greater. For example, in some embodiments, such a level of entropy may be achieved by combining two different sets (e.g., a first vector representing a 2048×2048 bit binary matrix and a second vector representing a randomly selected binary primitive polynomial of degree 256). 16 This can be achieved by a PNA object 314 implemented as a set of quantum proof (a vector representing the set of quantum proof and a second vector representing the set of quantum proof). It is understood that in some example implementations, a PNA object 314 with quantum proof can exhibit less entropy. It is understood that a PNA object 314 with 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 a PNA object (or other genomic dataset) that exhibits a lower level of entropy, requiring fewer storage and processing requirements, but at the expense 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.

[0163] In embodiments, PNA generation for genome eligibility synchronization 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 synchronization applications can be enabled based on specific mathematical principles with controllable entropy. Exemplary techniques for generating PNA objects and verifying engagement eligibility are discussed in more detail throughout this disclosure.

[0164] In an embodiment, a genome correlation object 320 may refer to a digitally generated mathematical object that allows VDAXs to establish correlations with one another. In an embodiment, the genome correlation object enables link exchange between VDAXs, whereby a first VDAX can generate a link (also referred to as a "link") that is provided to and hosted by a second VDAX, whereby the link provides 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 link exchange correlations, which allows two ecosystem components (e.g., an enclave VDAX, a cohort VDAX, etc.) to establish specific relationships and engage with one another.

[0165] In an example implementation of CG-ESP, the genomic correlation objects 320 of the digital ecosystem community members are implemented as LNA objects 322. In some embodiments, LNAs are the core capability on which the genomic correlation functionality relies. In embodiments, LNAs form the basis for VDAX to establish correlations with each other. The entropy exhibited by the LNA objects 322 is crucial in terms of the quality of the correlation. Non-recursive correlation attributes may be derived from specific computationally complex genomic functions. In some embodiments of CG-ESP, LNAs may be generated (e.g., by the ecosystem VDAX) for genomic correlation applications on large random datasets. 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 contribute to 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 can be enabled based on specific mathematical foundations with controllable entropy. In embodiments, LNAs may be generated according to a wide range of information-theoretic complex functions. In embodiments, these information-theoretic functions may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions.

[0166] In embodiments, a pair of VDAXs can perform 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 founder). In embodiments, a first VDAX may modify its LNA object and encode a genomic coordination instruction ("GRI") based on the modified LNAs, such that the second cohort is the only other VDAX that can decode the mapped GRI. In embodiments, a GRI may include data (e.g., one or more values) and instructions indicating how the data is used to distinguish the pair of VDAXs for data exchange. In embodiments, a GRI may be used to modify a differentiation object such that the data included in the GRI includes a differentiation value (e.g., embodied as a binary vector) that is used as an input parameter to a computationally complex function based on information theory that modifies the genomic differentiation object based on the differentiation value. In some embodiments, a GRI may include a sequence modification value used during the sequence mapping process. In these embodiments, the sequence mapping process may be used as an input parameter to a computationally complex information-theoretic function that modifies the sequence to an intermediate value based on the differentiation value, and then the intermediate value and the modified differentiation object are used as input values ​​to a computationally complex information-theoretic function that outputs a genome engagement value corresponding to the original sequence.

[0167] It is understood that encoding genome adjustment instructions based on modified LNAs may involve intermediate operations. For example, in some embodiments of CG-ESP, a VDAX may be configured to determine a mapping sequence, map the mapping sequence to the modified LNAs using a computationally complex function, and encode a GRI based on the genome engagement factor. In these exemplary embodiments, a VDAX can 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, such that link exchange 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, after a successful link exchange, even in some scenarios where the LNA objects of each VDAX are mutated (e.g., persistently modified), for example, by or at the direction of the founder VDAX, the cohort pair can continue exchanging data based on the respective links generated by each cohort. 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 exchange using LNA objects 322.

[0168] In embodiments of the present disclosure, a genomic differentiation object 330 may refer to a digitally generated mathematical object that allows pairs of community members (e.g., cohorts) to exchange and decode VBLS generated by the pair of community members, provided the pairs successfully exchange links and have sufficiently correlated genomic differentiation objects. In some embodiments, a first VDAX generates VBLS for a second VDAX in part by modifying its genomic differentiation object 330 in a manner defined in genomic coordination instructions (GRIs) provided to the first VDAX in links from the second VDAX, and decodes VBLS from the second cohort in part by modifying its genomic differentiation object 330 according to the GRIs provided to the second cohort. In CG-ESP embodiments, the first VDAX can use a computationally complex function (e.g., a cryptography-based, cryptography-less, or hybrid computationally complex function) to map a sequence (e.g., a private sequence or a public sequence) to a modified XNA object to obtain a genomic engagement factor, which is then used to encode the digital object. Examples of genome differentiation objects 330 include, but are not limited to, XNA332 objects and ZNA334 objects.

[0169] In an example, the genome differentiation object 330 of a digital ecosystem community member is an XNA. In some embodiments, XNA is the core capability on which all genome differentiation depends. In these embodiments, XNA forms the basis for the bi-symmetric language (e.g., VBLS) that VDAX employs to control inherent non-repeated engagement. In some embodiments, the inherent non-repeated engagement may be quantum-proof. In embodiments, the entropy exhibited by XNA is important from a VBLS security perspective, with higher entropy providing a higher level of security. In embodiments, recurring difference attributes are derived from specific computationally complex genome functions. 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 may be performed by a high-quality random process with controllable entropy. In some embodiments, XNA generation for genome differentiation applications may be enabled based on specific mathematical foundations with controllable entropy. In embodiments, XNAs may be generated according to complex functions based on extensive information theory. In embodiments, these information-theoretic functions may be cryptography-based, cryptography-less, or hybrid computationally complex functions.

[0170] In some embodiments, the XNA object 332 can 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 embodiments, the XNAs assigned to each community member (e.g., enclave member) from the progenitor 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 then maps 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.) can then be encoded using cryptography-based encryption or disambiguation and the genome engagement factor to obtain the 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 genome engagement factor. The resulting encoded digital object from VBLS is then provided to a second cohort. In these exemplary embodiments, the second cohort receives the VBLS object, modifies its XNA332 according to the GRI included in the link provided to the first VDAX from (or instead of) the second VDAX, and maps the sequence to the modified XNA to recreate the genome engagement factor. The genome engagement factor can then be used to decode the encoded digital object, and the decoded digital object can be obtained using the cryptography-based decryption or disambiguation used to encode the digital object.In these exemplary implementations, the ability of VDAX to modify each XNA object 332 using the same GRI and to 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 for each instance of data exchange (e.g., per packet, per sector, per shard, per frame, etc.). In this manner, VBLS can provide quantum-proof levels of security. Note that the foregoing discussion is one example of how XNA or other genome-differentiating objects may be leveraged in a secure data exchange process.

[0171] In some embodiments, revocation of a community member (e.g., a cohort) from a community (e.g., an enclave) can be achieved by a progenitor 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 to providing a descendant VDAX with a new XNA object. In this manner, the mutated XNA is used for subsequent VBLS encoding and encoding for the particular community. For example, in some implementations, an ecosystem VDAX can mutate the XNAs of only the cohorts that remain in the enclave. In this manner, a cohort that is revoked from the enclave can still attempt to engage with the cohort, but will not be able to generate VBLS for or decode VBLS from the cohorts that have the 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 a cohort, the enclave VDAX can mutate the cohort's XNAs so that they have XNAs that are sufficiently correlated with other community members whose XNAs were previously mutated, at which point the cohort can begin exchanging data with other cohorts in the enclave using those previously established links and / or future established links.

[0172] In an example implementation, a digital ecosystem community member's genome differentiation object 330 is a ZNA. In some embodiments, the ZNA is the core capability upon which all executable, separate-component genome differentiation relies. In these embodiments, the ZNA forms the basis upon which unique, non-recursive (potentially quantum-proof) executable binaries are controlled. EIC iterative transformations may be derived from specific, computationally complex genome functions. In embodiments, ZNA generation for genome differentiation applications results in large sets of random data that can be organized as specific binary vectors. 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 specific mathematical foundations with controllable entropy. In embodiments, ZNAs can be generated according to a wide range of information-theoretic complex functions. In embodiments, these information-theoretic functions may be cryptographically based, cryptographically less, or hybrid computationally complex functions.

[0173] 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 is 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 security of the VBLSs generated by community members. In embodiments, the ZNAs assigned to each community member (e.g., device component) from the progenitor 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.) can then be encoded with the complex function and the genome engagement factor to obtain an encoded digital object included in a VBLS object. In embodiments, the VBLS object can further include metadata, such as the sequence used to generate the genome engagement factor. The resulting encoded digital object from the VBLS is then provided to the second VDAX. In these 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 can then be used to decode the encoded digital object, yielding the decoded digital object using the inverse of the bidirectional function used to encode the digital object. In these example implementations, the ability of VDAXs to modify their respective ZNA objects 334 using the same GRI and determine the genomic engagement factor in a deterministic manner allows a first cohort to securely provide a data object to a second VDAX, potentially varying the genomic engagement factor for each instance of data exchange (e.g., per packet, per sector, per shard, per frame, etc.). In this manner, VBLS can provide quantum-proof levels of security. Note that the preceding discussion is an example of how ZNA or other genomic differentiating objects may be utilized in a secure data exchange process.

[0174] As can be appreciated from the present disclosure, core genomic capabilities (e.g., differentiation and correlation supporting CG-ESP processes) depend on the generation (e.g., DNA generation, which may include LNA generation, XNA generation, ZNA generation, CNA generation, and / or PNA generation), modification (e.g., DNA modification, which may include LNA modification, XNA modification, ZNA modification, CNA modification, and / or PNA modification), and assignment (e.g., DNA assignment, which may include LNA assignment, XNA assignment, ZNA assignment, CNA assignment, and / or PNA assignment) of a particular genome (e.g., digital DNA, which may include some combination of LNA, XNA, ZNA, CNA, and / or PNA). In embodiments, these application-specific DNA constructions (e.g., some combination of LNA, XNA, CNA, PNA, and / or ZNA) have specific transformations and are important for the controllable visualization of differentiation.

[0175] In embodiments, an ecosystem founder (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 modifications or updates to genomic data objects. For example, in embodiments, the ecosystem may mutate some or all of the ecosystem members' LNA objects 322, XNA objects 332, CNA objects 312, and / or PNA objects 314 so 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 persistent DNA objects 300, as opposed to modifications during link exchange or VBLS generation, which may be transient modifications. However, note that modifications and mutations may have similar effects on DNA construction, and that, where the context suggests, the term "modification" may be used in reference to persistent modifications.

[0176] In embodiments, community member LNA objects may be modified (e.g., for link exchange) and mutated (e.g., continuously modified / updated). As discussed, non-recurring correlation objects (e.g., LNAs) may be derived from specific computationally complex genomic functions, and this correlation may include a digital ecosystem with dimensions N×M, composed of VDAX with various enclave relationships N×Ma. Such digital ecosystem relationships may require modification of their correlation attributes to establish future or additional ecosystem relationships. LNA mutation allows for specific (broad and narrow) redetermination of correlation attributes. In some embodiments, LNA genome constructions can be tailored to specific digital ecosystem organizations, and the constructions are modifiable. In some embodiments, LNA random vectors can be modified uniformly or deliberately (broad and narrow) based on specific instructions. LNA modification preserves the genomic integrity of the LNA construction and its correlation attributes. In embodiments, a VDAX possessing a modified LNA cannot affect future correlations with a VDAX possessing an unmodified LNA. In embodiments, the LNA can be genomically modified according to a wide range of information-theoretic, cryptographically-based, computationally complex functions. In embodiments, these information-theoretic functions can be cryptographically-based, cryptographically-less, or hybrid computationally complex functions.

[0177] In embodiments, community members' XNAs may be mutated (e.g., continuously modified / updated). As discussed, non-recursive differentiating 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, composed of VDAXs with various enclave relationships N×Ma. Such digital ecosystem relationships may require modification of their differentiating attributes (e.g., relationship revocation), which represents one of the most challenging problems in security management. XNA mutation allows for specific (broad and narrow) redetermination of differentiating attributes, efficiently solving the relationship revocation challenge. In embodiments, XNA genome constructions can be tailored to specific digital ecosystem organizations, and the constructions are modifiable. In some embodiments, XNA random vectors can be modified uniformly or deliberately (broad and narrow) based on specific instructions. XNA modification preserves the genome integrity of the XNA construction and its correlation attributes. In embodiments, VDAXs with mutated XNAs cannot affect future differentiation from VDAXs with non-mutated XNAs. In embodiments, XNAs can be genomically mutated according to a wide range of information-theoretic, cryptographically-based, computationally complex functions. In embodiments, these information-theoretic functions can be cryptographically-based, cryptographically-less, or hybrid computationally complex functions.

[0178] In embodiments, a community member's CNA object 312 may be mutated (e.g., continuously modified / updated). As discussed, 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 include a digital ecosystem with dimensions N×M, composed of VDAXs with various enclave relationships N×Ma. Such digital ecosystem relationships may require modification of their differentiating attributes (e.g., revocation of relationships), which is one of the most challenging problems in security management. Modification of VDAX ecosystem eligibility objects preserves a common computationally complex genomic function. Such digital ecosystem relationships may require modification of their eligibility objects, preventing VDAXs from establishing future or additional ecosystem relationships. Mutation of a CNA or PNA allows for specific (broad and narrow) redetermining of the eligibility object.

[0179] In embodiments, CNA genome construction can be tailored to the organization of a particular digital ecosystem, and the construction is modifiable. In some embodiments, CNA random vectors can be modified uniformly or deliberately (broadly and narrowly) based on specific instructions. CNA modification preserves the genomic integrity of the CNA construction and its eligibility correlation attributes. In embodiments, VDAXs with mutated CNAs cannot establish future eligibility correlations with VDAXs with unmutated CNAs. In embodiments, CNAs can be genomically mutated according to a wide range of information-theoretically based, computationally complex functions. In embodiments, these information-theoretically based functions can be cryptographically based, cryptographically less, or hybrid computationally complex functions.

[0180] In embodiments, PNA genomic construction can be tailored to specific digital ecosystem organizations, and the construction is modifiable. In some embodiments, PNA random primitive polynomials can be modified uniformly or deliberately (broadly and narrowly) based on specific instructions. PNA modification preserves the genomic integrity of the PNA construction and its eligibility synchronization attributes. In embodiments, VDAXs carrying mutated PNAs cannot establish future eligibility synchronization with VDAXs carrying unmutated PNAs. In embodiments, PNAs can be genomically mutated according to computationally complex functions based on extensive information theory.

[0181] In embodiments, an ecosystem founder (e.g., ecosystem VDAX) can assign DNA to community members (e.g., enclaves, cohorts, etc.). In embodiments, each specific DNA construction 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 constructions are substantially derived from the relationship of their genomic mathematical constructions, ultimately resulting in the assignment of their specific VDAX. These VDAX relationships can be modified according to the specific modifications of the DNA (e.g., LNA, XNA, CNA, PNA).

[0182] In embodiments, the ecosystem founder (or appropriate founder VDAX) can assign LNAs to community members. In embodiments, LNA correlation capabilities are germane to all digital ecosystems with dimensions N×M, which are composed of VDAXs, which may have various enclave and cohort relationships N×M. In embodiments, LNA genome-based constructions are assigned to specific digital ecosystem VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to determine their associated correlation capabilities. In embodiments, LNA assignments preserve the genomic integrity of the LNA construction and its correlation attributes. In embodiments, a VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) whose initial LNA assignment has been corrected will no longer be able to influence correlations with VDAXs carrying uncorrected LNAs, but may be able to influence future correlations with other VDAXs with the same corrected LNA assignment. In embodiments, LNAs can be genomically assigned according to a wide range of information-theoretic, computationally complex functions, which in embodiments can be cryptographically based, cryptographically less, or hybrid computationally complex functions.

[0183] In embodiments, an ecosystem founder (or appropriate founder VDAX) can assign XNAs to community members. In embodiments, the differentiating power of an XNA is germane to an entire digital ecosystem with dimensions N×M, composed of VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs), which may have various enclave and cohort relationships N×Ma. In embodiments, XNA genome-based constructions are assigned to specific digital ecosystem VDAXs (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to determine their associated differentiating power. In embodiments, the assignment of an XNA preserves the genome integrity of the XNA construction and its differentiating attributes. In some embodiments, a VDAX (e.g., an ecosystem VDAX, an enclave VDAX, a cohort VDAX, etc.) whose initial XNA assignment has been modified may no longer be able to influence differentiation from VDAXs possessing unmodified XNAs, but may become able to influence differentiation from other VDAXs with the same modified XNA assignment. In embodiments, XNAs may be genomically assigned according to a wide range of information-theoretic, computationally complex functions. In embodiments, these information-theoretic functions may be cryptography-based, cryptography-less, or hybrid computationally complex functions.

[0184] In embodiments, the ecosystem founder (or appropriate founder VDAX) can assign CNAs to community members. In embodiments, the engagement alignment capabilities of CNAs are closely related to the entire digital ecosystem, which has dimensions N×M and is 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, CNA genome-based constructions 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 constructions determine their associated engagement alignment capabilities within the ecosystem. In some embodiments, the CNA genome-based constructions assigned to specific digital ecosystem VDAXs may also be unique.

[0185] In embodiments, CNA assignment maintains the genomic integrity of the CNA construction 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 corrected may no longer be able to influence engagement integrity with VDAXs harboring uncorrected CNAs, but may be able to influence engagement integrity with other VDAXs with the same corrected CNA assignment. In embodiments, CNAs may be genomically assigned according to a wide range of information-theoretic, computationally complex functions. In embodiments, these information-theoretic functions may be cryptographically based, cryptographically less, or hybrid computationally complex functions.

[0186] In embodiments, the ecosystem founder (or appropriate founder VDAX) can assign PNAs to community members. In embodiments, the engagement eligibility capabilities of a PNA are germane to the entire digital ecosystem, which has dimensions N×M and is 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, a PNA genome-based construction is assigned to a particular digital ecosystem VDAX (e.g., ecosystem VDAXs, enclave VDAXs, cohort VDAXs, and / or subordinate VDAXs) to determine their associated engagement eligibility capabilities. In embodiments, a PNA genome-based construction assigned to a particular digital ecosystem VDAX may also be unique.

[0187] In embodiments, PNA assignment maintains the genomic integrity of the PNA construction and its engagement eligibility attributes. A VDAX (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, etc.) whose initial PNA assignment has been corrected may no longer be able to influence engagement eligibility with VDAXs harboring uncorrected PNAs, but may be able to influence engagement eligibility with other VDAXs with the same corrected PNA assignment. In embodiments, PNAs may be genomically assigned to VDAXs according to a wide range of information-theoretic, computationally complex functions. In embodiments, these information-theoretic, functions may be cryptographically based, cryptographically less, or hybrid computationally complex functions. [Link Exchange]

[0188] As discussed, a pair of well-correlated VDAXs can engage using a link. In embodiments, the primary purpose of a link is to enable a pair of VDAXs to exchange information necessary to perform higher-level, computationally complex genomic functions. In embodiments, the information exchanged in the link is referred to as genome engagement cargo (GEC). In embodiments, the linking process includes link spawning, link hosting, and link updating. Link spawning may refer to the creation and transport of a link by the generating VDAX. Link hosting may refer to the acquisition and integration of the information contained in the link by the receiving VDAX. Link updating may refer to the CG process in which a VDAX modifies the genome basis used to engage with another VDAX. The process of link updating is sometimes referred to as "link correction." In embodiments, the linking process (spawning, hosting, updating) depends on the specific information-theoretic configuration. For example, in embodiments, LNAs may be used as the basis for genome correlation, CNAs may be used as the basis for genome engagement integrity, and PNAs may be used as the basis for genome engagement eligibility. These DNA constructs (e.g., LNA, CNA, and PNA) 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 the CG-ESP instances may be configured with link module 430 instances that perform these functions. For example, any VDAX whose role requires link creation, hosting, and / or updating can be configured with such a link module 430 instance that can define the static and / or dynamic linking processes.

[0189] 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) that seeks to create a genome link for receipt and use of a genome engagement cargo (GEC) by another VDAX (e.g., a second VDAX) utilizes its CNA to establish engagement alignment 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) creates a genome link for receipt and use of the contained GEC by the other VDAX (e.g., the second VDAX), thereby allowing a pair of VDAXs (e.g., a first and a second VDAX) to have multiple genome links that utilize the same CNA to establish engagement alignment.

[0190] 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) creates a genomic link for receipt and use of the included 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 that utilize the same PNA to establish engagement eligibility. Note that in some embodiments, the GEC included in the link can include additional link activation requirements.

[0191] In some embodiments, a generating VDAX generating a link for transmission and use (e.g., a "link host") by another VDAX (e.g., a second VDAX) can utilize its LNAs 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 construction (e.g., an LNA). In embodiments, the LNA-based genomic processes enable VDAXs to modify their respective LNA constructions by using specific computationally complex functions, such that these LNA-based genomic processes utilize subconstructions of genomic information (e.g., LNA-based genomic subconstructions). In embodiments, the LNA-based genomic subconstructions may be utilized to compute unique transformation information by the link generating VDAX that can only be reproduced by the link host 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 generating VDAX to the host VDAX. In some of these embodiments, the link host VDAX can 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 to multiple digital transport channels.

[0192] In some scenarios, ecosystem correlation is unavailable. In some embodiments, VDAX authentication may be required for link creation and hosting when ecosystem correlation is unavailable. In these embodiments, VDAX authentication may be achieved by using alternative genome subconstructions to facilitate free-form correlation (FFC). For example, a scenario may arise in which a pair of VDAXs are within unique genome digital ecosystems (sometimes referred to as "republics"). In some embodiments, these unrelated VDAXs may form unique genome digital ecosystems (sometimes referred to as "federations") for specific operations and uses. In these embodiments, VDAXs can create links not only within their respective republics but also as members of the federation.

[0193] In embodiments, the link generation genomics process may be performed according to a wide range of computationally complex functions that facilitate the execution of genomic functions and processes, which may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions.

[0194] As previously described, a genome link host (or "link host") may involve the acquisition and alignment of link information by a VDAX (e.g., a second VDAX) so that the link includes a specific genome engagement cargo (GEC) from another VDAX (e.g., a first VDAX). In embodiments, the link host may execute according to a specific computationally complex genome function. In embodiments, the host VDAX (e.g., a second VDAX) receives unique transformation information subconstructions via one or more digital transport channels. In embodiments, a host VDAX (e.g., a second VDAX) intended to use (host) the genome engagement cargo (GEC) transported by a link generated by a generating VDAX (e.g., a first VDAX) may utilize its CNA to establish engagement alignment with the generating VDAX. In an embodiment, a host VDAX (e.g., a second VDAX) intended to use (host) a genome engagement cargo (GEC) transported by a link generated by a generation VDAX (e.g., a first VDAX) can utilize its PNA to establish engagement eligibility with the generation VDAX.

[0195] In embodiments, a host VDAX can utilize LNAs that enable correlation of its digital ecosystem by modifying its LNAs using a specific computationally complex function that utilizes a unique transformation information subconstruction. In embodiments, the LNA-based genome subconstruction is utilized by a link host VDAX to calculate a unique genome engagement factor with the same level of entropy as the underlying computationally complex genome function. In embodiments, a host VDAX (e.g., a second VDAX) utilizes the unique genome engagement factor to extract a GEC from the link provided by a generating VDAX (e.g., a first VDAX). In embodiments, the host VDAX (e.g., a second VDAX) may be required to complete additional link activation requirements imposed by the generating VDAX, which provide the additional link activation requirements in the GEC.

[0196] As discussed, 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.

[0197] In embodiments, link host genome processes may be performed according to a wide range of computationally complex functions. In embodiments, these functions may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions. In embodiments, these functions may be necessary to perform genome operations.

[0198] In embodiments, a VDAX can update links hosted by other VDAXs. For example, to improve security, a VDAX can update links hosted by other VDAXs to reduce the likelihood that a malicious party can 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 previously completed the "link generation" and "link hosting" protocols can update one or both of their links. In this manner, a VDAX (e.g., a first VDAX) can modify 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 generated by a VDAX (e.g., a first VDAX) and sent to another VDAX (e.g., a second VDAX) for hosting can be used to replace one or more existing links hosted by the other VDAX with the newly generated link, thereby updating the link. In embodiments, a genome link generated by a VDAX and sent for hosting to another VDAX may be used to modify some or all of the GRI data of an existing hosted link.

[0199] As mentioned above, scenarios may arise where a pair of VDAXs are in unique genomic digital ecosystems (sometimes called "republics"). In some embodiments, these unrelated VDAXs may form unique genomic digital ecosystems (or "federations") for specific operations and uses. In some of these embodiments, a federation of VDAXs may also update their links for specific operations and uses.

[0200] In embodiments, the link update genome process may be performed according to a wide range of computationally complex functions, which may be cryptographic-based, cryptographic-less, or hybrid computationally complex functions necessary to perform genome functions and processes. [Sequence Mapping and Binary Conversion]

[0201] As discussed 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 sequences 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 information that relies on public protocols and formats (e.g., TCP, UDP, TLS, HTTP, H.265, etc.), and a private sequence may refer to information that relies on private and / or proprietary protocols and formats. In embodiments, a sequence (e.g., a public sequence or a private sequence) is computationally converted to a non-repeating value. While the sequence may be widely heterogeneous (e.g., protocol-independent and has pre-existing entropy), the sequence is processed in a manner that results in a value with a particular level of entropy. In embodiments, this process may begin with a variety of sequences that are compatible with a wide range of protocols and formats and exhibit their own pre-existing entropy. In embodiments, this process may be performed using complex genomic processes and functions that result in genomic engagement factors that exhibit a particular level of entropy.

[0202] In embodiments, CG-based security management systems and architectures may require the use of genome engagement factors in conjunction with genome data construction. In embodiments, these genome engagement factors may be derived in part through the use of repetitive data (e.g., sequences). Before the sequences are used with genome data construction, the sequences are processed so that the entropy of the resulting genome engagement factors matches the entropy of the genome construction (e.g., XNAs). This process is called "sequence mapping," and the product is called a genome engagement factor. Even if the sequences differ widely, the resulting genome engagement factors exhibit a certain level of entropy. In embodiments, genome engagement factors are produced 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 the transformation of specific digital objects, potentially including heterogeneous protocols and formats (e.g., TCP, UDP, TLS, HTTP, H.265).

[0203] In some embodiments, widely different external formats and protocol-resident data are used to construct sequences without modification. In some embodiments, widely different 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 constructions to determine unique vectors that exhibit specific entropy. In some embodiments, sequences are mapped according to computationally complex genome processes and functions in cooperation with specific genome data constructions to derive specific genome engagement factors. In embodiments, sequence mapping results in genome engagement factors that exhibit entropy that matches the entropy of the genome data constructions, regardless of the intrinsic entropy of the sequences. In some embodiments, genome engagement factors can be generated from sequence mapping that utilizes internal CG-ESP formats and protocols along 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 format- and protocol-resident data and genome-based constructions.

[0204] In embodiments, sequence mapping performs genome engagement factor-generating genome processing, computed according to computationally complex functions. In embodiments, these functions may be cryptographically based, cryptographically less, or hybrid computationally complex functions, whereby sequences (public or private) and XNAs generate unique genome engagement factors. Examples of sequence mapping are described in more detail below. [Hyper-scalability]

[0205] In some embodiments, CG-ESP can implement genomic processes to facilitate hyperscalable correlation. In embodiments, virtual validation of ecosystem, enclave, and cohort engagement relationships (e.g., unique correlations) can be achieved using hyperscalable correlation. As discussed, hyperscalability techniques can be used to powerfully enhance ecosystem, enclave, and cohort engagement, which relies on precise and unique correlations. As discussed, organic ecosystems (e.g., biological ecosystems) demonstrate bounded but strong correlations between species, offspring, and siblings that stem from complex biochemical processes. The principles governing these biochemical processes may be reflected by specific digital genome constructions facilitated by information theory, which exhibit unique correlations between ecosystems, enclaves, and cohorts. In embodiments, digital genome correlations are virtually unlimited and exhibit specific and user-controllable entropy. In embodiments, genome-qualified objects (e.g., CNAs and / or PNAs) and genome correlation objects (e.g., LNAs) can be used for digital genome correlation.

[0206] In embodiments, ecosystem VDAXs can leverage computationally complex genomic processes to achieve virtual affiliations with enclaves and cohorts. Similarly, enclave VDAXs can utilize these computationally complex genomic processes to achieve hyperscalable correlations with cohorts, and cohort VDAXs can 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 can modify the LNA of a given enclave to prevent future link exchanges in that particular enclave for one or more of the enclave's members. In some embodiments, enclave VDAXs and cohort VDAXs, which are members of a given ecosystem, can employ computationally complex genomic processes to correlate engagements with other enclave VDAXs and cohort VDAXs, which are members of the given ecosystem. For example, in some embodiments, two ecosystem VDAXs can form derived genomic datasets from their respective genomic datasets, allowing ecosystem members to engage across ecosystems using the derived genomic data (or its derivations). In this way, the enclave VDAX and 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 computationally complex functions, where PNAs, CNAs, and LNAs generate unique genomic engagement factors. These functions may be crypto-based, crypto-less, or hybrid computationally complex functions.

[0207] In some embodiments, CG-ESP can implement computationally complex processes to facilitate hyperscalable differentiation. In some instances, hyperscalable differentiation may be needed or required to provide unique affiliations between ecosystems, enclaves, and cohorts based on the relationships that digital networks foster. In embodiments, hyperscalability techniques can 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 strong, yet limited, differentiation between species, offspring, and siblings, which may result from complex biochemical processes. The principles governing these example biochemical processes may be reflected by specific digital genome constructions governed by information theory, which may exhibit unique differentiation between ecosystems, enclaves, and cohorts. In some instances, this digital genome differentiation may be virtually unlimited and may exhibit a specific, user-controllable entropy.

[0208] There may be various implementations 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 facilitates unique, non-recurring virtual alliances between ecosystems, enclaves, and cohorts. In some examples, CG-enabled enclaves may leverage computationally complex genomic processes to achieve hyperscalable differentiation that facilitates unique, non-recurring virtual alliances between enclaves and cohorts. In some examples, cohorts may leverage computationally complex genomic processes to achieve hyperscalable differentiation that facilitates unique, non-recurring virtual alliances between cohorts. In embodiments, the unique hyperscalable differentiation between ecosystems, enclaves, and cohorts may be modified 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 affiliate with enclaves and cohorts that are members of other ecosystems. In this way, according to some embodiments of the present disclosure, enclaves and cohorts may be able to achieve unique virtual alliances across multiple ecosystems based on computationally complex genomic processes. In some examples, hyperscalable differentiation can be performed by sequencing and XNA generating unique genomic engagement factors that can be used to generate VBLS, genomic processes computed according to a wide range of crypto-based, crypto-less, or hybrid (e.g., crypto-based and / or crypto-less) computationally complex functions.

[0209] In some embodiments, the CG-ESP can implement genome processes to facilitate virtual agility. In some examples, virtual agility can provide unique engagement between ecosystems, enclaves, and cohorts, which 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 are computationally expensive and can 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 construction, generated by a computationally complex process, that is virtually unbounded and exhibits a specific, user-controllable entropy.

[0210] There can be various example implementations for applying virtual agility in ecosystems, enclaves, and / or cohorts. For example, virtual agility can 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 can eliminate the requirement to create, negotiate, and maintain session-based protocols for the engagement of network communications, for the engagement of software applications, and / or for the engagement of hardware components. [Virtual Binary Scripting Language (VBS)]

[0211] In some embodiments, the CG-ESP can implement a computationally complex process for generating and / or decoding a VBLS. As described, the CG-ESP may be configured to perform link exchange (e.g., link creation and / or link hosting) and sequence mapping, which allows digital objects having specific formats and protocols (e.g., packets, sectors, sequences, and frames) to be computationally transformed into VBLS objects. In embodiments, the VBLS objects generated by this process may be unique, non-repeating, 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 facilitated by genomic information theory. A computationally quantum-proof VBLS can form the basis for building specific network, software, and hardware architectures, whether currently developed or newly deployed.

[0212] Various 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 completion 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 or bandwidth for VDAX(s) engagement. In some examples, VBLS-rendered objects may represent 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). By way of example, VBLS can facilitate unique, non-repeated, computationally quantum-proof engagements between community members (e.g., ecosystem-to-ecosystem, ecosystem-to-enclave, ecosystem-to-cohort, enclave-to-cohort, and / or cohort-to-cohort engagements) based on unique, computationally complex genome constructions 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 can form a unique relationship with each VDAX. In some embodiments, the genome engagement factors used for generation may be used simultaneously for primary and secondary applications that also require unique, non-repeated values ​​with a particular entropy.

[0213] In embodiments, VDAXs may be configured to engage in symmetric and / or bi-symmetric VBLS-based engagement. For example, in some embodiments, VBLS-enabled VDAX(s) may engage based on link exchanges (e.g., generated and hosted) using genome link instructions and genome constructions that may be identical, resulting in symmetric-based engagement. In embodiments, VBLS-enabled VDAXs may engage in bi-symmetric engagement based on highly correlated genome constructions (e.g., identical or sufficiently correlated XNAs). In these embodiments, VBLS-enabled VDAXs exchange links that include unique genome coordination instructions (GRIs). However, in some scenarios, VBLS-enabled VDAXs may engage in symmetric engagement if the link exchanges include identical GRIs. For example, a CG-ESP may be configured to perform unidirectional link exchanges, where one VDAX provides a GRI that is used by both VDAXs in the VBLS generation process. In this manner, a VBLS-enabled VDAX can engage with other VDAXs based on symmetric and / or bi-symmetric binary languages ​​without repeated coordination between the VDAXs. In some of these embodiments, VBLS-enabled VDAXs can proceed with engagement without negotiating a formal session because their symmetric or bi-symmetric binary languages ​​simultaneously encapsulate authentication, integrity, and privacy. Virtual Trusted Execution Domain

[0214] The rapid expansion of remote network-centric, highly distributed solutions and services (e.g., remote clouds and edge clouds) creates a situation where sensitive binaries may be executed in open or semi-open environments, exposing them to untrusted third parties (e.g., adversaries). Homomorphic cryptography (cogent processing of data in an encrypted state), functional obfuscation (cogent processing of data and application code in an encrypted state), and various trusted execution environments (e.g., physical and software separation of executable code) are current approaches to address these significant exposures. While these methods may provide significant improvements, each has a significant impact on performance, and no solution addresses the significant scalability required for widespread commercial application.

[0215] According to some embodiments of the present disclosure, CG-ESP 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 native 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, a CG-ESP executable isolation component (EIC) facilitates the component binary isolation (CBI) necessary for the required transformations. In some embodiments, isolation is enabled by 1) inherent genomic correlations between distributed components belonging to the same ecosystem and 2) inherent genomic differentiation from other executable ecosystem components. This correlation and differentiation process forms the foundation for virtual trusted execution domains (VTEDs) to achieve highly flexible and scalable component binary isolation (CBI).

[0216] 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) can be achieved through the sharing of genome components within a VTED and their CBI with a VTED ecosystem VDAX, 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.

[0217] 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.

[0218] In embodiments, the VTED-transformed executable binary is unique, non-repeating, and computationally quantum-proof. In embodiments, this transformation eliminates the requirement for private 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 genome construction (e.g., LNA or ZNA) to build the transformed executable binary.

[0219] In embodiments, VTED's hyperscalable correlation, hyperscalable differentiation, and hyperscalable agility uniquely enable CBIs to operate with minimal overhead and bandwidth. In further embodiments, a vast number of genome constructions are applied to a vast number of CBIs, providing hyperscalability for the VTED ecosystem.

[0220] In embodiments, VTED-enabled CBIs for ecosystems, enclaves, and cohorts can be directly genomically modified without compromising binary executable relationships. In embodiments, VTED-enabled CBIs modify binary information while preserving the ability of VTED to execute CBIs within its ecosystem.

[0221] In embodiments, VTED-enabled CBIs may be compatible with known cryptographic-based and cryptographic-less computational methods. In further embodiments, compatibility of VTED and CBIs with cryptographic-based and cryptographic-less computational methods is maintained through transparent genome construction-based transformations.

[0222] In embodiments, VTED can enable unique, non-repeatable, computational quantum proof of CBI feasibility across specific ecosystems based on unique computationally complex genome construction and processes. In further embodiments, the CBI construction process applies genome construction that does not rely on traditional computationally expensive operations.

[0223] In embodiments, a VTED may enable CBI executables to have numerous properties, including unique, non-repeatable, computationally quantum-proof engagement between specific ecosystems and enclaves. In embodiments, a CBI executable exhibits these properties based on its unique, computationally complex genome construction and processes. In further embodiments, a VTED applies a genome construction to the deployment of a CBI executable that enables quantum-proof interaction between the CBI executable and the genome VTED.

[0224] In embodiments, VTED can enable CBI executables based on unique, computationally complex genome constructions 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-repeating, and quantum-proof. In further embodiments, Ecosystem VDAX can provide a genome-construction-based CBI licensing model where individual cohorts can have specific features or CBIs enabled for operation within that ecosystem, enclave, or cohort. [CypherGenics-enabled digital ecosystem and architecture]

[0225] Referring now to FIG. 6 , an exemplary CG-enabled digital ecosystem 600 is depicted in accordance with some exemplary embodiments of the present disclosure. It should be noted that the exemplary 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,” this term may refer to an entity that manages, maintains, or owns the community, its representative (e.g., a network administrator, CIO, IT administrator, homeowner, consultant, 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.

[0226] 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 "security controller." In an embodiment, the CG-enabled digital ecosystem 600 includes a set of enclaves 602 and a respective set of cohorts for each enclave. Note that a general reference to a CG-ESP may also refer 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 discussed, 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, 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 a digital container-based VDAX or for which the independent cohort serves as a surrogate. 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 an enclave. For example, the mutual identity of interest may be a set 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 topography of the digital community (and the architecture of the corresponding CG-ESP) may be defined by a community owner, taking into account these mutual identities of interest. In embodiments, the eligibility for membership to ecosystem 600 and / or its one or more enclaves 602 may be defined by the community owner, and membership and its 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 default settings, such that a community owner purchases or otherwise obtains a digital ecosystem pre-configured with default settings.

[0227] In some embodiments, an ecosystem founder (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 associated with the CG-enabled digital ecosystem 600 may be defined by a CG-ESP (e.g., as discussed in FIG. 4). In these embodiments, VDAXs participating in the digital ecosystem 100 may each run a respective instance of a CG-ESP, such that each VDAX runs a CG-ESP instance such that the CG-ESP instance enables the VDAX to perform 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 that fulfills an ecosystem-level role, one or more Enclave VDAXs 610 that fulfill an enclave-level role, one or more Cohort VDAXs 612 that fulfill a cohort-level role, and / or one or more Subordinate VDAXs 614 that fulfill subordinate cohort roles. For example, in some example implementations, an Ecosystem VDAX 608 may be configured to generate, assign, and permanently modify genomic data for other ecosystem members (e.g., via a CG-ESP instance), verify eligibility for engagement, exchange links, and generate VBLS, while a Cohort VDAX 612 does not have the ability to create or assign genomic data for the ecosystem to other cohorts (e.g., via a Cohort CG-ESP instance), but is configured to verify eligibility for engagement, exchange links, and generate VBLS.

[0228] 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.

[0229] In embodiments, Ecosystem VDAX 608 may be considered the "founder" of the ecosystem because it performs security-related functions for the digital ecosystem and does not require any subsequent interaction with the enclave or a cohort of enclaves after Ecosystem VDAX 608 is initialized and assigns the enclave its genomic dataset. Note that in embodiments, an ecosystem may be configured to allow for independent sub-ecosystems and may include multiple lower-level VDAXs that have functional ecosystem-level VDAX capabilities but are derived from the primary Ecosystem VDAX 608.

[0230] In embodiments, ecosystem VDAX 608 digitally generates each genomic dataset for one-time distribution to ecosystem enclaves 602 and / or cohorts 604, 606 within each enclave 602. Genomic data objects within a genomic dataset may have similar or identical constructions, 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, and / or the like), which governs the ability of a member to establish engagement with another member. In embodiments, genome differentiation objects (e.g., XNA or ZNA objects) provide the capability for VBLS-based member-to-member symmetric communication. In embodiments, the digital genome construction of CNAs, PNAs, LNAs, and XNAs is complex and unique. In embodiments, CNAs, LNAs, XNAs, and PNAs may be derived using complex mathematical functions.

[0231] According to some embodiments of the present disclosure, Ecosystem VDAX 608 can 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 progeny genomic datasets of the digital ecosystem 600 are derived. For ease of description, the genomic dataset of the ecosystem founder may be referred to as the “founder genomic dataset” (or “founder DNA set”). In some embodiments, Ecosystem VDAX 608 can initially generate the founder genomic dataset. For example, ecosystem VDAX 608 may generate, for each progenitor genome data object, a respective binary vector having a particular dimension.

[0232] In some embodiments, ecosystem VDAX 608 may generate respective descendant genome datasets for each enclave from the founder genome dataset. In some embodiments, ecosystem VDAX 608 may modify the founder genome dataset using a set of predefined genome operations to obtain descendant genome datasets (or "enclave datasets") that are then propagated to each enclave. For example, ecosystem VDAX 608 may modify the founder genome eligible object of the founder genome dataset using a computationally complex function to obtain different enclave genome eligible objects for each enclave in the ecosystem; may modify the founder correlation object of the founder genome dataset using a computationally complex function to obtain different enclave correlation objects for each enclave in the ecosystem; and may modify the founder differentiation object of the founder genome dataset using a computationally complex function to obtain different enclave differentiation objects for each enclave in the ecosystem. In embodiments, the techniques by which different types of genome objects are modified may differ because different genome objects may be implemented in different types of data structures and / or may be required to exhibit different properties. Different correction techniques are described throughout this disclosure. Note that in some implementations of the security platform, there may only be a single enclave. Depending on the various techniques implemented in a particular CG-ESP, some 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 community member but still be sufficiently correlated.Note that even if some types of genomic objects in the descendant genomic dataset are not modified from the corresponding genomic objects in the founder 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., enclaves or cohorts) may be referred to as "derivation," and a descendant genomic dataset (e.g., an enclave genomic dataset or a cohort genomic dataset) may be said to be derived from an founder genomic dataset (e.g., an founder genomic dataset or an enclave genomic dataset) even if one or more genomic objects in the descendant genomic dataset are not modified from the founder genomic dataset.

[0233] 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, enclave VDAX 610 may configure CG-ESP to generate unique but highly correlated genomic eligible objects (e.g., CNAs) for each independent cohort 604 added or to 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 pair of cohorts within the enclave have 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 object of the founder (e.g., ecosystem or enclave) so that the cohorts are unique while maintaining a high degree of correlation. In this manner, any pair of cohorts can confirm their eligibility to participate with one another 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, a pair of cohorts can authenticate each other based on each cohort's respective genomic correlation object and can distinguish themselves from other cohorts based on each cohort's respective genomic differentiation object. In embodiments, the genomic correlation object and genomic differentiation object of a cohort may be separate objects (although these objects 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.

[0234] Note that while in some embodiments, a cohort's genomic dataset is assigned to only one entity (e.g., a device, document, sensor, etc.), in other embodiments, the community owner may permit the cohort's genomic dataset to be cloned 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 their employment. 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 the same access rights with respect to their respective enclave 602. Note that in some of these embodiments, each device with a cloned genomic dataset still must independently qualify, authenticate, and / or exchange links with other cohorts in the enclave 602.

[0235] Note 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 enables the VDAX to use the correct genomic functions when performing genomic operations such as eligibility correlation, link generation, link hosting, sequence mapping, LNA correction, XNA correction, and binary object conversion. 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 the community member. In these embodiments, the GCP may be used in confirming eligibility for cohort engagement.

[0236] In some embodiments, cohort VDAX 612 may be configured to perform genome security operations and processing on behalf of independent cohort 604. In some embodiments, cohort VDAX 612 facilitates data exchange with sufficiently correlated community members (e.g., other cohorts within enclave 602). In some of these embodiments, facilitating data exchange with other community members may include verifying engagement eligibility (e.g., engagement alignment and engagement synchronization) and exchanging links with other respective community members (e.g., another independent cohort 604). In some embodiments, verifying engagement eligibility and exchanging links is a one-time process; once a pair of cohorts successfully completes this "handshake," the pair of VDAXs can securely exchange data as long as they continue to share highly correlated (e.g., identical or otherwise sufficiently correlated) differentiation objects. For example, a pair of VDAXs can initially confirm their eligibility for engagement and exchange a link, and the VDAXs can continue to communicate securely for days, weeks, months, or even years unless they no longer share a common differentiating object. Once the cohorts no longer share a common differentiating object, they can attempt to exchange data, but will no longer be able to decode the encoded digital objects provided by each of the other cohorts.

[0237] In embodiments, a pair of VDAXs interact with each other through a virtual binary language script (VBLS) generated and decoded by each VDAX. As described, a 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., a cohort VDAX 612 or an enclave VDAX 610) may generate a VBLS object for a second VDAX (e.g., a cohort VDAX 612 or an enclave VDAX 610) based on the genomic coordination instructions (GRI) encoded in a link provided by the second VDAX to the first VDAX and the first VDAX's genomic data (e.g., XNA). In these embodiments, the second VDAX can receive the VBLS object from the first VDAX and can decode the VBLS based on the GRI and the VDAX's genomic 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 an encoded digital object (e.g., a 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).

[0238] In an embodiment, the first VDAX can generate a VBLS object corresponding to the digital object 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, at the link provided by the second VDAX, modifies the genome differentiation object according to the GRI provided by the second VDAX and maps the 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 the 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 cryptographically-based function, a non-cryptographically-based function, or a hybrid function). The first VDAX can then use the genome engagement factor to 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) to obtain an encoded digital object. In an embodiment, a first VDAX utilizes a computationally complex function (e.g., an encryption function or a disambiguation / XOR function) to encode a digital object based on the genome engagement factors. The first VDAX can then provide a VBLS object containing the metadata (e.g., sequence) and the encoded digital object to a second VDAX (e.g., via a network and / or data bus).

[0239] In an embodiment, the second VDAX can 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 differentiation object. In an embodiment, the second VDAX is configured to extract a sequence from the VBLS object (e.g., an unencrypted public sequence or private sequence portion 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), and the second VDAX uses the same computationally complex function to map the sequence (or a value derived therefrom) to the modified genome differentiation object 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 decoding function or a disambiguation / XOR function) to decode the digital object based on the same genomic engagement factor. In this way, the first VDAX and the second VDAX can differentiate themselves in a unique way from other community members who share the same genomic differentiating object, because other community members who do not possess the link provided from the second VDAX to the first VDAX cannot modify their genomic differentiating object in the same way. Thus, even if other community members are configured to perform the same computationally complex mapping function and are able to determine the public sequence, other community members will not be able to generate genomic engagement factors.Interloping or other malicious devices without access to genomic data are further limited because they may not know the computationally complex functions used to generate the genomic engagement factor, the method for extracting sequences, or one or more of the common genomic differentiating objects. Therefore, they cannot determine the genomic engagement factor except by brute force. Furthermore, because the CG-ESP can be configured so that the first VDAX calculates a new genomic engagement factor for every digital object (e.g., every data packet, shard of a file, video frame, audio frame, etc.), each encoded VBLS object requires a separate brute force determination of the digital object, making the VBLS generated by the first VDAX for the second VDAX quantum-proof.

[0240] In some embodiments, the metadata in the VBLS object can further include data integrity information. For example, the data integrity information is a value that the first VDAX calculates on the plain data and uses as a sequence. In this way, the second VDAX can verify that the VBLS object has not been tampered with.

[0241] 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 that is uniquely understood by the receiving enclave or cohort, i.e., a language understandable only by the receiving enclave or cohort. In this way, an interloper seeking to include unauthorized cohorts, viruses, and / or malware cannot generate or decipher a VBLS between authorized cohorts.

[0242] It should be understood that the foregoing discussion is provided as an example of a CG-enabled digital ecosystem 600. It is understood that different configurations of CG-ESP can perform different functions and operations and can 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, etc. Furthermore, it should be noted that CG-ESP can be configured for different ecosystems, enabling different architectures.

[0243] Figures 7-12 illustrate various types of digital ecosystems and corresponding architectures. Modern network functionality substantially reflects its underlying deployment architecture. In embodiments, a CG-enabled architecture that uses genome construction to realize VBLS operates at the bit level, thereby maintaining interoperability with the underlying deployment architecture. VBLS provides unprecedented facilities and flexibility to uniquely tailor the application of network, software, and / or hardware-centric architectures. Examples of CG-ESP ecosystem architectures include, but are not limited to, directed architectures that support static ecosystems, free-form architectures that constitute transient ecosystems, spontaneous architectures that support dynamic ecosystems, ephemeral architectures that support executable ecosystems, and Interledger that supports verification ecosystems. In embodiments, these architectures, which may overlay existing physical network topologies, attest to genome-constructed topologies. In some embodiments, multiple genome-constructed topologies may exist simultaneously and interoperably. For example, a computing device may be a viable ecosystem, where internal components of the computing device exchange VBLS, and at the same time, the computing device may be a member of a static ecosystem, where the computing device may engage with other devices in the static ecosystem using different sets of genomic data.

[0244] Referring to FIG. 7, a directed architecture may be implemented in a static ecosystem because the characteristics of such an ecosystem and the enclaves and cohorts participating in it exhibit a fairly stable configuration. 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 are unlikely to change significantly over time. These ecosystems provide stability of relationships without losing or inhibiting flexibility. These architectural attributes are uniquely extended and strengthened so that they can function independently. In some exemplary embodiments, a static architecture, as opposed to a free-form architecture, may enable specification in the way correlations are performed and managed. In a static architecture, correlations are achieved based on a common genome construction provided by VDAX in a single ecosystem.

[0245] In embodiments, a directed architecture reflects a configuration in which an ecosystem VDAX establishes one or more enclaves that exhibit particular genomic correlations and differentiation. In some of these embodiments, each enclave VDAX may correspondingly establish one or more cohorts that also exhibit particular 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, a directed architecture, ecosystem, enclave, and cohort VDAX may have multiple genomic correlation and differentiation attributes. For example, an enclave in a directed architecture may propagate to both lower enclaves and cohorts. In embodiments, different architectures may be configured to exhibit different correlation characteristics. For example, a directed architecture may inherently exhibit common correlations, while a free-form architecture may exhibit arranged common correlations.

[0246] In embodiments, genomic correlation and differentiation enables a directed architecture for configuring genomic network topologies without the need to modify the physical topology. For example, in these embodiments, a community owner can control the engagement of cohorts in different enclaves using different LNAs and XNAs, such that engagement (e.g., via link exchange) between cohorts in different enclaves is prevented by the community by controlling the LNAs and / or XNAs provided to members of different enclaves. Similarly, in these examples, a community owner can create new enclaves by controlling the LNAs and XNAs provided to different cohorts.

[0247] In embodiments, the genome topology enabled by the directed architecture can be modified over time, genomically. In some of these embodiments, a community owner can periodically modify the specific genome constructions (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.).

[0248] 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 require seamless performance and security. While such deployments are common in enterprise-class organizations and institutions, security is a challenge for small and medium-sized businesses (SMBs) due to their complexity. Such implementations tend to be relatively static and centrally managed. For example, in a static ecosystem, a business unit may include 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 those employees are typically granted access to a different 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) can define a set of policies that define the type of access that individual cohorts can be granted with respect to specific files or folders, the enclave or enclaves to which each cohort belongs, the cohorts and / or enclaves (e.g., printers, local file servers, etc.) that each cohort can digitally engage with, and / or other appropriate policies. As discussed, such policies can be implemented using genome constructions, such as XNAs, CNAs, PNAs, and LNAs, which can be used to define allowable relationships and genome topologies across the ecosystem.

[0249] 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 defines N enclaves 704, including a first enclave 704-1 and an Nth enclave 704-N, in a hierarchical manner (e.g., a directional architecture). For each enclave 704-1 to 704-N, the ecosystem VDAX 702 can create an enclave VDAX (which runs the enclave VDAX) for the enclave 704-1 and 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, etc. 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 broader 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 other cohorts 706 within the enclave 704 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 provider of the CG-ESP. Furthermore, note 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 VDAX (not shown) to acquire genomic material correlated to designated ecosystem cohorts, thereby enabling authentication, linking, and engagement between the ecosystem and the third-party application systems / cloud facilities. Additionally or alternatively, a community owner may determine that specific third-party service providers (e.g., cloud services) may be added to the ecosystem as cohorts, and the community owner may 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 can only exchange links with other cohorts (e.g., potential 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 via XNA modification.

[0250] In some embodiments, an enclave VDAX (or ecosystem VDAX) of an enclave 704 may generate and assign genomic material to the VDAX of each cohort 706 within 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 the 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 genome construction, two cohort VDAXs admitted to an enclave may be required to participate in a link exchange. If two cohort VDAXs join a link exchange, they can begin exchanging VBLS based on their hosted link(s).

[0251] In a directed architecture, an ecosystem owner (e.g., via ecosystem VDAX 702) can manage security features of enclaves 704 and / or cohorts 706 within enclaves 704 by initiating modifications to the XNAs and / or LNAs of the cohorts 706 and / or enclaves 704. For example, if a particular 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 can "revoke" an employee's access to a cohort (e.g., workstations, mobile devices, etc.) by initiating modifications to the XNAs (and in some scenarios, LNAs) of enclaves 704 and / or cohorts 706 that remain in ecosystem 700, without initiating the same XNA modifications to the cohorts 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 can initiate remediation of the XNAs in the second folder with the DNA and LNAs of other cohorts in the enclave without providing remediation to the cohort(s) of the employee whose access to the second folder was revoked. In these provided examples, community owners can use genome constructions (e.g., LNAs and / or XNAs) to control the engagement of cohorts in different enclaves.

[0252] It should be appreciated that the foregoing discussion provides some example implementations of directional architectures, and that CG-ESP may be configured according to directional architectures in other suitable ecosystems without departing from the scope of this disclosure.

[0253] 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., a limited number of users, devices, etc.) is fairly stable but may change according to the mutual identity of their interests (e.g., changes to the ecosystem are less predictable than in a static ecosystem). Thus, a free-form architecture can provide stability of relationships without losing or inhibiting flexibility. In some embodiments, these architectural attributes may be independently extended and enhanced, as they alone enable. In embodiments, a provision that a free-form architecture may enable, as opposed to a static architecture, 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, but are achieved, for example, by using alternative free-form genome subconstructions to facilitate common genome correlations.

[0254] 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., LNAs and / or XNAs). In some embodiments, enclave VDAXs and / or cohort VDAXs in a directed architecture can directly acquire their unique genome correlation constructions (e.g., LNAs). For example, an ecosystem VDAX can control which cohorts belong to which enclaves through the creation and modification of genome constructions.

[0255] In embodiments, ecosystem VDAXs in a free-form architecture can acquire their unique genome correlation constructions through alternative genome subconstructions. In some of these embodiments, each ecosystem VDAX can have a unique genome construction from which correlation and differentiation attributes are derived. In some of these embodiments, these derived attributes control the genome topology of an enclave in the ecosystem. In some of these embodiments, each enclave (e.g., through its enclave VDAX) may have a unique genome construction from which correlation and differentiation attributes are derived, and these attributes control the genome topology of the enclave's cohort (e.g., cohort VDAX). In embodiments, multiple genome construction 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 a topology that may, in some scenarios, depend solely on the VBLS facilitated genome construction.

[0256] 8 illustrates an example of a security platform with a free-form architecture that provides services to an interactive ecosystem 800. In an embodiment, the interactive ecosystem 800 may include one or more enclaves 804 (e.g., a personal residence, a home office, a small business, etc.), each of which may include one or more cohorts 806 (e.g., computers, home appliances, hubs, media devices, IoT devices, wearable devices, smart speakers, etc.), which share a mutual identity of interest and can interact with a wide range of network-enabled web portals (e.g., Facebook, Amazon, banking servers, healthcare servers, etc.) whose services and applications are interactive but require user-controlled security. Examples of interactive ecosystems 800 include home networks, small office networks, etc.

[0257] In a free-form architecture, a cohort 806 within ecosystem 800 can be designated as a VDAX 802 of ecosystem 800. For example, a user can designate a mobile device, desktop, or router to function as a VDAX 802 of ecosystem 800. Additionally, through VDAX 802, a user can define one or more enclaves (e.g., via a user interface). For example, in some situations, a user can define a single enclave 804 (e.g., all devices associated with the user). In another example, a user can 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 embodiments, a user can define one or more settings (e.g., rules, policies, blacklists, whitelists, etc.) for an enclave 804 and / or individual devices through VDAX 802. These parameters can be used when generating genomic data (e.g., XNAs, LNAs, CNAs, and / or PNAs) for an enclave 804 or 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 XNAs, LNAs, PNAs, and / or CNAs for the cohorts 806 within the enclave 804.

[0258] In interactive ecosystem 800, the external systems that cohort 806 may access are diverse. For example, a user may use a workstation or mobile device to access a web portal, stream video, access social media platforms, visit websites, read emails and messages, open attachments, and the like. Similarly, a user may have devices in their home that are capable of motion detection, audio recording, video capture, or recording sensor measurements or other data related to the user and their home or office. These devices also access a web portal to report data or utilize the web portal's services (e.g., ordering products, adjusting thermostats, etc.). In the former example, a user may be concerned about privacy and / or malicious software (e.g., viruses or malware) being installed on the device. In the latter example, a 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 can 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 can 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 pair of VDAXs to confirm eligibility matches and / or synchronicity and ultimately exchange links. Once the web portal 810 and cohort 806 generate and exchange links, the cohort 806 and web portal 810 can each host the other's links.Cohort 806 can use links generated and hosted by web portal 810 to generate VBLS that are sent to web portal 810, and web portal 810 can use links generated by cohort 806 to generate VBLS that are sent to cohort 806. The foregoing example is merely one example of a free-form architecture, and other implementations are within the scope of this disclosure.

[0259] Referring now to FIG. 9 , a spontaneous architecture may be implemented to support applications and services that undergo highly dynamic changes in metric (e.g., time, data, state, request, coordinates, actions, relative position, and events) states. For example, an autonomous vehicle management system may manage an ecosystem of autonomous vehicles moving across a control grid where the control grid 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 be provided for an air traffic control system, a military theater, or a drone swarm where cohorts are constantly changing and may have highly dynamic security responses to the environment. In embodiments, the spontaneous topology may change by modifying the DNA in response to situational events. To respond to these situational events, modified DNA may be dynamically distributed to various cohorts or groups.

[0260] 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 state or operator preference(s). In certain situations, network architectures are required to address the additional challenge of being 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, enabling support for emerging ultra-bandwidth applications or artificial intelligence portals. Further applications of spontaneous architectures include theaters of war, power grid management, or highly distributed financial trading systems.

[0261] In embodiments, Ecosystem VDAX can construct and control a genome network topology that supports applications requiring dynamic state attributes. In embodiments, Ecosystem VDAX can control engagement of cohorts within the ecosystem using different genome constructions (e.g., LNAs and XNAs) such that engagement (e.g., via link exchange) between cohorts within the ecosystem can be enabled, prevented, and / or revoked by a community owner by controlling the LNAs and / or XNAs provided to different members. Similarly, in these examples, Ecosystem VDAX can change the dynamic network topology of the ecosystem by controlling the LNAs and XNAs provided to different cohorts.

[0262] In embodiments, enclave VDAXs can be configured to control their respective portions of the ecosystem's genome network topology, such that the enclave VDAX is responsible for specific VDAX-designated functions and processes related to its portion of the genome network topology. In further embodiments, genome correlation and differentiation enable enclave VDAXs to configure dynamic genome network topologies without the need to modify the physical topology. For example, in these embodiments, an enclave VDAX can control the engagement of cohorts in different enclaves using different genome constructions (e.g., LNAs and XNAs) such that engagement (e.g., via link exchange) between cohorts in different enclaves can be dynamically enabled, prevented, or revoked by the enclave VDAX by controlling the LNAs and / or XNAs provided to different enclave members. Similarly, in these examples, an enclave VDAX can create new dynamic spontaneous enclaves by controlling the genome constructions (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, a Cohort VDAX can be responsible for performing specific functions for its respective portion of the genome network topology designated by the Ecosystem VDAX and / or Enclave VDAX. In further embodiments, a Cohort VDAX performing such functions can enable a spontaneous 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 genome constructions (e.g., LNAs and XNAs) to control engagement of VDAXs for designated portions of the genome network topology, such that engagement between VDAXs (e.g., via 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.

[0263] In embodiments, various types of interactions (e.g., ecosystem VDAX vs. enclave VDAX, enclave VDAX vs. cohort VDAX, ecosystem VDAX vs. cohort VDAX, and / or cohort VDAX vs. cohort VDAX interactions) may be controlled by specific genome constructions (e.g., CNAs, PNAs, LNAs, and XNAs) determined by the ecosystem VDAX. For example, in these embodiments, the ecosystem VDAX can control engagement between VDAXs corresponding to respective portions of the genome network topology by dynamically modifying some or all of the genome constructions (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 can be added, blocked, and / or revoked from different portions of the genome network topology via their respective genome constructions. For example, in some embodiments, enclave VDAXs designated to control respective portions of the genome network topology can alter that portion of the genome network topology by selectively modifying the genome constructions of VDAXs that are added and / or withdrawn from their respective portions of the genome network topology. In embodiments, genome constructions (e.g., CNAs, PNAs, LNAs, and / or XNAs) responsible for ecosystem, enclave, and cohort engagement may be modified to change the basis for differentiation and / or correlation, which modifies the genome network topology. In some embodiments, such modifications are performed as part of updating the genome network topology (e.g., withdrawing a cohort).In embodiments, these genome constructions may be modified to allow control over engagement of cohorts within the ecosystem that use different genome constructions (e.g., LNAs and XNAs), such that engagement between members within the ecosystem (e.g., via link exchange) can be enabled, prevented, or canceled by controlling the genome construction. Similarly, in these examples, ecosystem CNAs, PNAs, LNAs, and / or XNAs can change the dynamic network topology of the ecosystem by controlling the genome constructions provided to different cohorts.

[0264] In embodiments, the spontaneous architecture maintains its operational integrity regardless of the dynamic frequency of the metric states (e.g., time, data, state, request, adjustment, action, or event) that it supports. For example, in some embodiments, a cohort may operate in an environment where the reporting frequency of metrics may fluctuate. In these embodiments, the spontaneous architecture handles these fluctuations in the overall metric data while maintaining the overall integrity of the ecosystem.

[0265] FIG. 9 illustrates an example of a security platform with a spontaneous architecture that serves a dynamic ecosystem 900. In an embodiment, the dynamic ecosystem 900 includes enclaves and their cohorts that support applications and services that undergo highly dynamic changes in state (time, data, conditions, demand, adjustments, actions, etc.). Examples of dynamic ecosystems 900 include artificial intelligence applications, autonomous driving 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 such ecosystems are such that traditional cryptographic protocols cannot support dynamic frequencies and cannot accommodate various conditions. In an embodiment, the security platform is implemented as a spontaneous architecture that supports the spontaneous ecosystem 900. Such an architecture holds great promise for emerging integrations between ultra-high bandwidth and artificial intelligence (AI) portals.

[0266] In a voluntary 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 may not be assigned to a specific enclave 904. When a cohort 906 joins the ecosystem 900, VDAX 902 and the Al portal 910 can 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 Al 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 modifications 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 the membership of enclaves 904 within ecosystem 900 in this manner so that cohorts 906 within grid 912 maintain a high level of correlation with other cohorts 906 within enclave 904, and so that cohorts 906 that are no longer within grid 912 no longer maintain a high level of correlation.

[0267] In the illustrated example, the voluntary ecosystem 900 is an environment for autonomous vehicles. In such an environment, vehicles may traverse the roads of a region (e.g., an entire city, an entire state, etc.). At times, hundreds of thousands of vehicles may traverse the roads, and at other times, fewer vehicles may traverse the roads. Each vehicle is configured to report its sensor data (e.g., LIDAR, radar, video, moisture, etc.) to a cloud-based system, which may maintain status data related to the roads (e.g., the location of vehicles, obstacles, traffic, etc.). The cloud-based system may be configured to report relevant status data to each vehicle to inform the vehicle of conditions along its route (or other appropriate data, such as instructions for a particular vehicle). Because each vehicle travels along a unique path, the amount of data collected per second from a collection of vehicles can be enormous. In the illustrated example, VDAX 902 and AI Portal use VBLS to facilitate the reporting of relevant status data to vehicles along the grid. In this example, VDAX 902 can 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 a fixed size or may be dynamically sized depending on the amount of traffic on the road. Similarly, the number of cells may be fixed or may be 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 are traditionally on the road at this time, etc.). In embodiments, each cell is considered an enclave 904, and a cloud-based system can report relevant status data to vehicles within the enclave 904. In some embodiments, a communication tower (e.g., a 5G tower) can host an enclave VDAX that communicates with cohorts 906 within the enclave 904. As a vehicle crosses a road, 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 status data for one or more cells in front of the vehicle, one or more cells to the right of the vehicle, and one or more cells to the left of the vehicle. VDAX902 can provide the vehicle with genome information for each of these enclaves (e.g., one or more cells to the right, left, and front of the vehicle). For each cell / enclave, the vehicle (e.g., the cohort VDAX running on it) can 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 can exchange links to engage for each cell. As the vehicle collects sensor data, it generates a VBLS based on the collected sensor data, its XNA, and the 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 modifies the vehicle's genomic information for that cell so that the vehicle can no longer understand or generate the VBLS corresponding to that cell.

[0268] The application ecosystem continues to evolve, featuring complex services and processes that require more abundant resource availability and lower latency networks. This is evidenced by process redistribution and infrastructure reallocation. 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: the proliferation of very low-cost client devices that retain access to powerful non-resident capabilities, very low bandwidth costs, and the free-flowing 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 property control.

[0269] Referring now to FIG. 10, by implementing computationally complex genome construction, CG-ESP enables a method for uniquely transforming engagement between various software and hardware components, “executable ecosystems,” such as applications (APIs, libraries, and threads), operating systems (kernels, services, drivers, and libraries), and systems-on-chip (processing units such as cores). These ecosystem components can execute executable binaries collaboratively or independently. In embodiments, the method may enable such ecosystems and enclaves, as well as cohorts (independent and dependent)—those best attending their capabilities, limitations, and performance efficiencies—to be specifically specified and organized to form a gnomically constructed ephemeral architecture. In embodiments, the ephemeral architecture can transform executable binaries into VBLS digital objects and resulting VBLS streams, which exhibit unique genomic differentiation and correlation. In some of these embodiments, the transient architecture of CG-ESP is capable of computationally complex genome construction that facilitates engagement with other CG-enabled architectures, including directed (static ecosystems and free-form ecosystems) and spontaneous (dynamic states).

[0270] In embodiments, ephemeral architectures may offer many advantages in that many of their attributes exhibit direct correlation with other architectures (e.g., directed and spontaneous). However, ephemeral 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, these hypothetical varying conditions may benefit from the dynamic virtual trusted execution domains that VBLS facilitates.

[0271] In embodiments, genomic correlation and differentiation enable a transience architecture that can be genomically configured by VDAX to enable an executable ecosystem (e.g., ecosystem VDAX, enclave VDAX, cohort VDAX, and subordinate VDAX). In further embodiments, an executable ecosystem based on the transience architecture is provided, in which VDAXs at different hierarchical levels provide deep source knowledge that enables 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, is composed of a vast array of components, each of which is composed of further subcomponents; in this example, each tier of the ecosystem enables a respective VDAX and genomic construction to build a system of component source knowledge. In further embodiments, each component can perform operations to verify the source and operational accuracy of its subcomponents. In this example, operational accuracy can be underwritten by genomic constructible exchange with trusted sources.

[0272] 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 ephemeral architecture can be applied to validate the operational parameters of the executable ecosystem.

[0273] In embodiments, an executable ecosystem VDAX can independently initiate its own genome correlation construction. In further embodiments, the correlation construction can provide verification of the attribution of subcomponents within the enclave VDAX. In some embodiments, the correlation construction can include LNA (Genome Correlation), CNA (Genome Engagement Consistency), and / or PNA (Genome Engagement Eligibility). In this example, these constructions enable virtual authentication.

[0274] In embodiments, an executable ecosystem VDAX may independently initiate its own genome differentiation construction (e.g., ZNA). In some of these embodiments, the differentiation construction initiated by the executable ecosystem may be applied to determine which components are responsible for specific operations within the ecosystem and / or that components are isolated. In some embodiments, the differentiation construction may include ZNA (genome code isolation). In this example, these differentiations may enable virtual alliances.

[0275] In embodiments, a viable ecosystem VDAX can directly obtain its own genome correlation construction. In further embodiments, the correlation construction can provide validation of subcomponent attribution within a genome progeny VDAX, such as an enclave VDAX or a cohort VDAX. In some embodiments, the correlation construction can include LNA (genomic correlation), CNA (genomic engagement integrity), and / or PNA (genomic engagement eligibility). In this example, these constructions can enable virtual authentication.

[0276] In embodiments, the executable enclave VDAX can directly obtain its own genomic differentiation construction. In further embodiments, the differentiation construction initiated by the executable enclave may be applied to determine which component is responsible for a particular operation, or whether the component is alone. In some embodiments, the differentiation construction may include ZNA (genomic code isolation). In this example, these differentiations may enable virtual alliances.

[0277] In embodiments, an executable enclave VDAX can directly obtain a unique genome correlation construction. In further embodiments, the correlation construction can provide validation of subcomponent membership within a genome-decedent VDAX, such as a subordinate VDAX or cohort VDAX. In some embodiments, the correlation construction can include LNA (Genome Correlation), CNA (Genome Engagement Consistency), and / or PNA (Genome Engagement Eligibility). In this example, these constructions can enable virtual authentication.

[0278] In embodiments, a viable cohort VDAX can directly obtain its unique genome correlation construction. In further embodiments, the correlation construction can provide validation of the attribution of subcomponents within a genome-generated VDAX, such as a dependent VDAX. In some embodiments, the correlation construction can include LNA (genomic correlation), CNA (genomic engagement integrity), and / or PNA (genomic engagement eligibility). In this example, these constructions can enable virtual validation.

[0279] In embodiments, the executable cohort VDAX may directly acquire its unique genomic differentiating construction. In further embodiments, executable enclave-initiated differentiating construction may be applied to determine which component is responsible for a particular operation or the decision that a component is isolated. In some of these embodiments, executable ecosystem-initiated differentiating construction may be applied to determine which component is responsible for a particular operation within the ecosystem and / or the decision that a component is isolated. In some embodiments, differentiating construction may include ZNA (genomic code isolation). In this example, these differentiating constructions may enable virtual alliances.

[0280] In embodiments, executable VDAXs (involving unrelated VDAXs) can obtain their unique genome correlation constructions through alternative genome subconstructions. In further embodiments, correlation constructions can provide verification of the attribution of unrelated components. In some embodiments, correlation constructions can include LNA (Genome Correlation), CNA (Genome Engagement Completeness), and / or PNA (Genome Engagement Eligibility). In this example, these constructions enable virtual authentication.

[0281] In embodiments, executable VDAXs (involving unrelated VDAXs) can acquire their unique genome differentiating constructions through alternative genome subconstructions. In further embodiments, executable enclave-initiated differentiating constructions may be applied to determine which components are responsible for determining that a particular operation or component is isolated. In some of these embodiments, executable ecosystem-initiated differentiating constructions may be applied to determine which components are responsible for determining that a particular operation and / or component within the ecosystem is isolated. In some embodiments, differentiating constructions may include ZNA (genome code isolation). In this example, these differentiating constructions may enable virtual alliances.

[0282] In embodiments, there may be multiple executable genome topologies constructed simultaneously. In some embodiments, these multiple genome 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.

[0283] In embodiments, each viable ecosystem has a unique genome construction from which correlation and differentiating attributes are derived, which control the genome topology of that enclave. In some embodiments, each viable enclave may have a unique genome construction from which correlation and differentiating attributes are derived, which control the genome topology of its cohort. In further embodiments, these unique constructions provide differentiation between species, offspring, and siblings.

[0284] In embodiments, ephemeral architectures with various genomically engineered configurations can exchange binary data as VBLS-based digital objects or streams. In embodiments, the ephemeral architecture security platform provides virtual agility. In embodiments, VBLS may refer to a language spoken by one enclave or cohort to another enclave or cohort that is uniquely understood by the receiving enclave or cohort, i.e., a language that only the receiving enclave or cohort understands. In this example, an intruder attempting to contain unauthorized cohorts, viruses, or malware, cannot generate or decipher VBLS between authorized cohorts.

[0285] In embodiments, VBLS-transformed binary data may be exchanged and acted upon by components from two or more different configurations that have common genomic correlations and differentiations. In embodiments, the different configurations provide state information for the operational parameters of the executable ecosystem. In further embodiments, these different configurations may each operate according to their state and functional components with knowledge of the associated configuration.

[0286] In embodiments, the transient architecture may have multiple genomically constructed configurations, where certain components may be capable of converting executable binaries into Virtual Binary Language Script (VBLS)-based digital objects or streams. In further embodiments, the executable binaries may be converted into VBLS digital objects or VBLS streams, where this conversion is achieved by application of genomically constructed configuration components. In further embodiments, the conversion is achieved by applying genomic sequence mapping and conversion. In embodiments, the sequence is central to the computational conversion of the digital object into a unique, non-repeating genomic engagement element. In examples, the sequence may be widely heterogeneous, and the sequence may require processing to bring about 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 by computationally complex genomic processes and functions.

[0287] In embodiments, a VBLS-transformed executable binary may be swapped and executed by components from two or more different configurations that have common genomic correlations and differentiations.

[0288] In embodiments, within a particular ephemeral architecture, a component can translate a VBLS executable binary (e.g., a proprietary computer application) such that the translated executable binary can only be correctly processed by a particular hardware component (e.g., an SoC core), which components share common genomic correlations and differentiations. In further embodiments, the particular hardware is part of a genomic ecosystem and can apply genomic correlation processes that enable the processing of VBLS executable binaries.

[0289] In embodiments, a VDAX resident component of a transient architecture can translate a (VBLS) executable binary (e.g., a proprietary computer application) such that the translated executable binary can only be correctly processed by a VDAX-specific hardware component (e.g., an SoC core) of another transient architecture, which components share common genomic correlations and differentiations. In further embodiments, another transient architecture VDAX may be part of a genomic ecosystem.

[0290] In embodiments, within a particular transient architecture, two or more components may transform executable binaries (e.g., proprietary computer applications) based on unique genome constructions, which are known to another component. These transformed binaries may be re-formed as executable binaries only by one of these components, and not by the others. Reconstruction and execution of the executable binaries occurs in-place. In embodiments, the components are part of the same genome ecosystem or genome enclave.

[0291] In embodiments, within a particular ephemeral architecture, a particular component can translate an executable binary (e.g., a proprietary computer application) based on a unique genome construction known to a particular component of another ephemeral architecture. A translated binary from one architecture can be reconstructed as an executable binary only by a particular component of another architecture. Reconstruction and execution of the executable binary occurs in-place. In further embodiments, ephemeral a...

Claims

1. 1. A method of executing a computer program in a trusted execution environment of a device, comprising: obtaining a genomic differentiation object corresponding to a set of one or more computer programs including a first computer program, the first computer program including a first set of encoded computer executable instructions, the genomic differentiation object including a binary vector of a particular length; modifying the genomic differentiation object based on a first genomic coordination instruction (GRI) to obtain a first modified genomic differentiation object, the first GRI being used to encode the first set of encoded executable instructions of the first computer program; obtaining a first coded instruction to be executed from the first set of coded computer-executable instructions of the first computer program; obtaining a first sequence from first metadata associated with the first encoded instructions; generating a first genome engagement factor (GEF) based on the first sequence and the first modified genome differentiation object; decoding the first encoded instructions using the first GEF to obtain first decoded instructions; and executing the first decoded instructions.

2. obtaining second coded instructions to be executed from the first set of coded computer-executable instructions of the first computer program; obtaining a second sequence from second metadata associated with the second encoded instructions; generating a second GEF based on the second sequence and the modified genome differentiation object; decoding the second encoded instructions using the second GEF to obtain second decoded instructions; and 2. The method of claim 1, further comprising: executing the second decoded instructions.

3. modifying the genomic differentiation object with a second GRI corresponding to a second computer program of the set of computer programs to obtain a second modified genomic differentiation object, the second computer program including a second set of encoded computer executable instructions; obtaining third coded instructions to be executed from the second set of coded computer-executable instructions of the second computer program; obtaining a third sequence from third metadata associated with the third encoded instructions; generating a third GEF based on the third sequence and the second modified genome differentiation object; decoding the third encoded instructions using the third GEF to obtain third decoded instructions; and 2. The method of claim 1, further comprising: executing the third decoded instruction.

4. 4. The method of claim 3, wherein the genomic differentiation object is assigned to both the first computer program and the second computer program.

5. 5. The method of claim 4, wherein the first GRI corresponds only to the first computer program and the second GRI corresponds only to the second computer program.

6. 5. The method of claim 4, wherein the first computer program and the second computer program, and any other computer programs in the set of computer programs, are distributed by the same entity.

7. 7. The method of claim 6, wherein the entity assigns the genomic differentiation object to the first computer program and the second computer program.

8. 4. The method of claim 3, wherein the VDAX of the device assigns the genomic differentiation object to the first computer program and the second computer program.

9. 9. The method of claim 8, wherein the VDAX generates the first GRI and the second GRI.

10. 2. The method of claim 1, further comprising installing the first computer program prior to decoding the first coded instructions and the second coded instructions.

11. Installing the first computer program includes: obtaining a set of uncoded computer-executable instructions comprising the first computer program; allocating memory on a computer readable storage medium of said device to said first computer program; generating the first GRI and associating the first GRI with the first computer program; modifying the genomic differentiation object based on the first GRI to obtain the modified genomic differentiation object; and generating the first set of encoded computer-executable instructions; Generating the first set of encoded computer-executable instructions includes, for each unencoded instruction in the set of unencoded computer-executable instructions: determining a respective sequence from respective metadata associated with each of the unencoded instructions; generating respective GEFs based on the respective sequences and the modified genome differentiation objects; encoding, using the respective GEF, the respective unencoded instructions to obtain respective encoded instructions of the first set of encoded computer-executable instructions; and 11. The method of claim 10, wherein the method is implemented by storing each encoded instruction in an allocated memory location in the memory.

12. 12. The method of claim 11, wherein the respective metadata from which the respective sequences are extracted includes one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata.

13. the execution environment being a processing device, 2. The method of claim 1, wherein the processing device includes a separate executable component VDAX that is a dedicated core of the processing device for decoding encoded instructions, and a set of processing cores that execute the decoded instructions.

14. 1. A method of executing a computer program in a trusted execution environment of a device, comprising: obtaining a genomic differentiation object corresponding to a set of one or more computer programs comprising the computer program; modifying the genomic differentiation object based on a genomic adjustment instruction (GRI) to obtain a modified genomic differentiation object; executing first executable instructions of the computer program; and executing second executable instructions of the computer program; Executing the first executable instructions includes: obtaining first encoded data input to the first executable instructions; extracting a first sequence from first metadata associated with the first encoded data; generating a first genome engagement factor (GEF) based on the first sequence, the GRI, and the modified genome differentiation object; Decoding the first encoded data based on the first GEF to obtain first decoded data; and executing the first executable instructions using the first decrypted data; Executing the second executable instructions includes: obtaining second encoded data input to the second executable instructions; extracting a second sequence from second metadata associated with the second encoded data; generating a second GEF based on the second sequence, the GRI, and the modified genome differentiation object; Decoding the second encoded data based on the second GEF to obtain second decoded data; and executing the second executable instructions using the second decrypted data.

15. Executing the first executable instructions includes: Obtaining output data resulting from execution of the first executable instructions; generating a third GEF based on a third sequence corresponding to the output data, the GRI, and the modified genome differentiation object; and 15. The method of claim 14, further comprising: converting the output data into a third encoded data based on the third GEF.

16. 16. The method of claim 15, further comprising: writing the third encoded data to a memory.

17. 17. The method of claim 16, wherein the third sequence is extracted from metadata corresponding to the output data, the metadata including one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata.

18. executing third executable instructions of the computer program; Executing the third executable instructions includes: obtaining the third encoded data input to the third executable instructions; extracting the third sequence from values ​​representing the metadata corresponding to the output data; generating the third GEF based on the third sequence, the GRI, and the modified genome differentiation object; Decoding the third encoded data based on the third GEF to obtain the output data; and 20. The method of claim 17, wherein the method is accomplished by executing the first executable instructions using the output data resulting from execution of the first executable instructions.

19. 15. The method of claim 14, wherein the first metadata from which the first sequence is extracted and the second metadata from which the second sequence is extracted comprise one or more of random access memory (RAM) metadata, application metadata, operating system application metadata, and file system metadata.

20. Producing the first GEF comprises: generating a sequence transformation vector (SCV) based on the first sequence and the first GRI; Mapping the modified genomic differentiation object based on the SCV to obtain a mapped modified genomic differentiation object; and 15. The method of claim 14, further comprising generating the first GEF based on the mapped modified genome differentiation object.

21. 21. The method of claim 20, wherein generating the SCV comprises combining the first sequence with the GRI.

22. 22. The method of claim 21, wherein generating the SCV further comprises applying one or more computational functions to the combination of the first sequence and the GRI to obtain the SCV.

23. 15. The method of claim 14, wherein decoding the first encoded data includes decoding the first encoded data using the first GEF, and decoding the second encoded data includes decoding the second encoded data using the second GEF.

24. 15. The method of claim 14, wherein decoding the first encoded data includes disambiguating the first encoded data using the first GEF, and disambiguating the second encoded data includes decoding the second encoded data using the second GEF.

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