Data index secure access method and related equipment

Abstract

The invention discloses a data index secure access method and related equipment, and relates to the technical field of data processing.The method comprises the steps that in response to a data access instruction, a dynamic index topological structure matched with a current network delay jitter value is generated based on a dynamic behavior fingerprint of a request terminal; performing real-time reconstruction on the dynamic index topological structure based on spatial and temporal distribution characteristics of the dynamic behavior fingerprints to generate an anti-collision verification path; a quantum noise signal is injected into the anti-collision verification path to form a cross-layer verification channel; performing asymmetric hash chain verification on the target data index through a cross-layer verification channel, and generating a verification result containing a fuzzy topology mark; performing dynamic remapping on the original index node according to the fuzzy topology mark, and outputting a security data index; and performing data access based on the secure data index. According to the method, the dynamicity, the unpredictability and the anti-analysis capability of the index path are realized, and the security and the anti-attack capability in the data access process can be comprehensively improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing. More specifically, this application relates to a data index security access method and related devices. Background Art

[0002] With the rapid development of technologies such as big data, cloud computing, and the Internet of Things, cross-network transmission and distributed access of data have become increasingly frequent. How to improve the security during the data index process while ensuring access efficiency has become an important research topic in the field of information security; especially in scenarios with high data protection requirements such as finance, telecommunications, government affairs, and military industries, the predictability of the index path and the resolvability of data identifiers are extremely likely to become breakthrough points for attackers; therefore, constructing a dynamic, tamper-proof, and difficult-to-replicate data index security access mechanism is of great significance for ensuring the overall security of the system.

[0003] In the existing related technologies, the data index structure generally adopts a static design pattern, with a fixed index path and a single verification process. Attackers can predict, tamper with, or steal the data index through methods such as path collision, data replay, and behavior simulation. Especially when facing threats such as fake terminals and advanced persistent threats, the protection capabilities of traditional methods are significantly insufficient; in addition, the current data verification mechanism generally lacks multi-level dynamic adaptability and mostly relies on static rules or symmetric verification algorithms, making it difficult to resist security risks such as man-in-the-middle attacks, side-channel eavesdropping, and path parsing in complex environments. That is to say, there are generally technical problems such as easy data leakage and ineffective access control in the related technologies. Summary of the Invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section of this application, which will be further detailed in the Detailed Description section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0005] The data index security access method and related devices provided by this application can achieve the dynamics, unpredictability, and anti-parsing ability of the index path by integrating multiple mechanisms such as dynamic behavior fingerprints, network features, quantum noise, cross-layer verification, and fuzzification remapping, and can comprehensively improve the security and anti-attack ability during the data access process.

[0006] In a first aspect, the present application provides a method for secure access to data indexing, including: in response to a data access instruction, generating a dynamic index topology structure that matches the current network latency jitter value based on the dynamic behavior fingerprint of the requesting terminal; performing real-time reconstruction on the dynamic index topology structure based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint to generate an anti-collision verification path; injecting a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel; performing asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result including a blurred topology marker; dynamically remapping the original index nodes according to the blurred topology marker to output a secure data index resistant to parsing; and performing data access based on the secure data index.

[0007] In some embodiments, the generating a dynamic index topology structure that matches the current network latency jitter value based on the dynamic behavior fingerprint of the requesting terminal includes: obtaining the touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; performing non-linear fusion on the touch trajectory characteristics and network fluctuation characteristics through a convolutional chaos algorithm to generate a topology generation factor; and constructing the dynamic index topology structure in the complex domain space according to the topology generation factor, wherein the dimension parameter of the dynamic index topology structure is dynamically adjusted by the current network latency jitter value.

[0008] In some embodiments, the performing real-time reconstruction on the dynamic index topology structure based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint to generate an anti-collision verification path includes: decomposing the spatio-temporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the space dimension; calculating a dynamic correlation matrix according to the entropy value of the sequence fluctuation characteristics and the variance of the trajectory distribution characteristics; performing real-time adjustment on the connection weights of the dynamic index topology structure based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; and screening the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retaining the paths that meet the preset anti-conflict conditions as the anti-collision verification path, wherein the anti-conflict conditions include that the path conflict probability is less than a first preset threshold and the path jump count is greater than a second preset threshold.

[0009] In some embodiments, injecting a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel includes: obtaining path parameters of the anti-collision verification path, where the path parameters include path length, node distribution density, and hopping frequency; generating a quantum noise signal matching the path parameters based on a quantum random number generator, where the spectral width of the quantum noise signal is positively correlated with the hopping frequency; performing convolution processing on the quantum noise signal and the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; injecting the phase perturbation factor into the boundary between the physical layer and the logical layer of the anti-collision verification path to form a cross-layer verification channel, where the verification data packet of the cross-layer verification channel carries a noise fingerprint in the physical layer and embeds a dynamic hash tag in the logical layer.

[0010] In some embodiments, performing an asymmetric hash chain verification on a target data index through the cross-layer verification channel to generate a verification result including a blurred topological tag includes: constructing an asymmetric hash chain structure based on the dynamic hash tag and the phase perturbation factor, where the public key of the asymmetric hash chain structure is generated from the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated from the expiration timestamp of the dynamic hash tag in the logical layer; performing cross-verification on the node attributes of the target data index to generate an initial verification result; performing iterative signing on the initial verification result using the asymmetric hash chain structure to obtain a multi-level verification signature; comparing the multi-level verification signature with a preset anti-interference threshold, and if the threshold condition is met, extracting the topological distribution characteristics of the multi-level verification signature; performing blurred encoding on the topological distribution characteristics based on a random masking factor in the quantum noise signal to generate the blurred topological tag, where the generation process of the blurred topological tag is dynamically associated with the dimensional parameters of the dynamic index topological structure.

[0011] In some embodiments, dynamically remapping the original index nodes according to the blurred topological tag and outputting a secure data index resistant to parsing includes: analyzing the quantum noise spectrum characteristics and topological distribution parameters in the blurred topological tag to generate an anti-parsing mapping rule; dynamically offsetting the storage location and access permission tags of the original index nodes based on the anti-parsing mapping rule to generate an implicit mapping relationship; performing non-linear confusion on the implicit mapping relationship according to the dimensional parameters of the dynamic index topological structure and the current system timestamp to generate a randomized index mapping table; injecting a dynamic confusion factor into the randomized index mapping table to generate the secure data index resistant to parsing, where the generation frequency of the dynamic confusion factor is synchronously updated with the hopping frequency of the quantum noise signal.

[0012] In some embodiments, the data index security access method further includes: when detecting an abnormal parsing behavior for the security data index, generating a virtual decoy index set according to the distribution characteristics of the obfuscated topology tags, and covering the virtual decoy index set to the original index nodes; dynamically adjusting the topology parameters of the virtual decoy index set based on the jump frequency of the quantum noise signal to generate a confusion index network with an attack traceability function; when a virtual node in the confusion index network is activated for access, extracting the behavior fingerprint features of the attacker and triggering a data self-destruction instruction for the distributed storage node.

[0013] In a second aspect, the present application further provides a data index security access device, including: a topology structure generation unit, configured to generate a dynamic index topology structure matching the current network delay jitter value based on the dynamic behavior fingerprint of the request terminal in response to a data access instruction; a verification path generation unit, configured to perform real-time reconstruction on the dynamic index topology structure based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint to generate a collision-resistant verification path; a verification channel generation unit, configured to inject a quantum noise signal into the collision-resistant verification path to form a cross-layer verification channel; a topology tag generation unit, configured to perform an asymmetric hash chain verification on a target data index through the cross-layer verification channel to generate a verification result including obfuscated topology tags; a data index generation unit, configured to perform dynamic remapping on the original index nodes according to the obfuscated topology tags and output a security data index resistant to parsing; and a data access unit, configured to perform data access based on the security data index.

[0014] In a third aspect, the present application further provides an electronic device, including: a memory and a processor, where the processor is configured to implement the steps of the data index security access method described in the first aspect when executing a computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, where the computer program, when executed by a processor, implements the steps of the data index security access method described in the first aspect.

[0016] In a fifth aspect, the present application further provides a computer program product, including a computer program or computer-executable instructions, where the computer program or computer-executable instructions, when executed by a processor, implement the data index security access method provided in the embodiments of the present application.

[0017] In summary, the present application utilizes the dynamic behavior fingerprint and network latency characteristics of the requesting terminal to construct a dynamic index structure, which can dynamically respond to different user environments, making it difficult for illegal terminals to reproduce the access patterns of legal terminals and effectively avoiding the problem that static structures are easily predictable or replicable. By analyzing the spatio-temporal distribution characteristics of the dynamic behavior fingerprint, the index structure is adjusted in real time, which can prevent different requests from falling into the same index path, prevent the data index path from being collided or enumerated, and effectively defend against brute-force cracking and path speculation attacks. Using quantum noise signals to disrupt the signal characteristics of the verification path and create uncertainty can render traditional electronic monitoring and side-channel analysis methods ineffective, enhancing the concealment and randomness of path verification. By constructing a verification mechanism through cross-network-layer channels, attackers cannot crack the system layer by layer, enhancing the hierarchical security of the system. Using an asymmetric hash chain can achieve data anti-tampering and request traceability, improving the consistency and credibility of access behavior and index content. Using fuzzy processing to conceal the real index structure can prevent attackers from reverse-inferring the original topology structure by analyzing the verification results, and further strengthening the protection mechanism through dynamic remapping to ensure that the index cannot be reverse-reconstructed even if it is intercepted. In summary, the data index secure access method provided by the present application realizes the dynamics, unpredictability, and anti-parsing ability of the index path by integrating multiple mechanisms such as dynamic behavior fingerprint, network characteristics, quantum noise, cross-layer verification, and fuzzy remapping, and can comprehensively improve the security and anti-attack ability during the data access process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be a limitation of this specification. Also, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 is a schematic flowchart of a data index secure access method provided by an embodiment of the present application; Figure 2 is a schematic structural diagram of a data index secure access device provided by an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The terms in the description, claims, and drawings of this application, such as "first", "second", "third", "fourth", etc. (if any), are used to distinguish similar objects and do not describe a specific order or sequence. Therefore, it is understood that under appropriate circumstances, these terms can be used interchangeably, so that the described embodiments can be arranged in different orders, unless there are special requirements in the drawings or description. In addition, the terms "is" and "has" in this application and any of their variants are intended to non-exclusively cover all possible constituent elements. For example, a process, method, system, product, or device that includes several steps or units does not necessarily have to be limited to the steps or units explicitly listed, but may also include other steps or units not explicitly listed, or steps or units inherent to the process, method, product, or device.

[0020] In this application, a "module" or "unit" refers to a computer program or a part of a computer program with a specific function, and works in cooperation with other related parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as processing circuits or memories), or a combination of both. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be a part of a larger module or unit.

[0021] The technical solutions in this application will be described in detail below with reference to the drawings in the embodiments. It should be noted that the described embodiments are only a part of this application, rather than all embodiments. In the following description, the "some embodiments" mentioned are only subsets of all possible embodiments, which can be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0022] Figure 1 is a flowchart showing a method for secure access to data indexing provided by an embodiment of this application. Exemplarily, see Figure 1 The method for secure access to data indexing provided by the embodiment of this application may include the following steps 101 to 106: Step 101, in response to a data access instruction, generate a dynamic index topology structure that matches the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal; In some examples, data read (query) or write (upload) requests initiated by the user or application of a data access instruction can be triggered through system calls, API requests, front-end operation instructions, etc., such as when the user clicks "View File". The requesting terminal refers to the device that initiates the data access instruction, which may be a mobile phone, a computer, an Internet of Things terminal, etc., and can be identified by the unique identification information of the terminal, such as device ID, MAC address, IP address, etc. The dynamic behavior fingerprint is a combination of fine-grained behavior characteristics exhibited by the requesting terminal within a certain time window, which can be used to identify and dynamically model user behavior. The dynamic behavior fingerprint can include touch trajectories, sliding speeds, pressing forces, click intervals, retry times, etc., and can be collected through the front-end SDK or the behavior perception module. The current network latency jitter value is the data transmission latency fluctuation of the network within a certain time period, which is an important indicator of network stability and can be calculated in real time through sending ICMP ping packets, measuring the RTT of HTTP requests, the RTTStats interface in WebRTC, etc.; for example, the latency is tested every 100 ms within 1 second, and the difference between the maximum and minimum recorded latencies is the jitter value, such as 30 ms. The dynamic index topology structure is a dynamically generated data index graph structure, and the construction rules of its nodes and edges are jointly driven by the user behavior fingerprint and the network state, with real-time and unpredictability, and can be automatically constructed by an algorithm engine (such as a chaos algorithm, a graph structure generator) in combination with the foregoing fingerprints and network parameters.

[0023] Exemplarily, when the user initiates a request to view the encrypted file in the cloud (i.e., the data access instruction), it is identified that the terminal is an Android device, and its touch trajectory is recorded as "fast L-shaped slide, light touch click 3 times" within a 3-second time window; combined with its current network jitter value of 15 ms, this behavior and the network fluctuation characteristics are fused through the convolutional chaos algorithm to generate a dynamic index topology structure.

[0024] Through the implementation of step 101, an index structure is dynamically generated in combination with the behavior characteristics of the user terminal and the real-time network state, making the index personalized and time-sensitive, difficult to be counterfeited or reproduced by attackers, effectively avoiding the problems that traditional static index structures are easy to be predicted and reused, and enhancing the system's anti-invasion ability from the source.

[0025] Step 102, based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint, perform real-time reconstruction on the dynamic index topology structure to generate an anti-collision verification path; In some examples, the spatio-temporal distribution feature refers to the dynamic distribution characteristics of the dynamic behavior fingerprint in the time dimension and the space dimension. The time dimension is reflected in the timing law and rhythm intensity of the occurrence of behavioral events (such as click frequency, sliding rhythm), and the space dimension is reflected in the change of operation position, trajectory range, path direction, etc. Based on the existing dynamic index topology structure, its node connection, path priority or structural form can be dynamically updated according to the current spatio-temporal behavior characteristics to form a new index topology structure. The anti-collision verification path refers to the path with a low path overlap probability, a high node jump diversity, and a strong randomness selected in the reconstructed index topology structure for subsequent security verification operations. The screening criteria of "the path collision probability is less than a certain threshold and the path jump count is higher than another threshold" can be used, and the path selection algorithm is executed by combining the node access history on the path and the perturbation factor of the reconstructed graph. For example, in the reconstructed index topology structure, there are path A and path B. Path A has been requested 3 times in the past 1 second and the jump count is 1; path B has no access record in the past and the jump count is 3. Then path B is preferentially selected as the anti-collision verification path.

[0026] Exemplarily, based on the initial dynamic index graph generated in step 101, further collect the rhythm of the user's behavior on the time axis (such as the operations are concentrated in the first 1 second) and the trajectory range in the space dimension (such as concentrated in the upper right area of the screen), decompose these spatio-temporal features into time entropy and spatial variance, and construct a dynamic correlation matrix; subsequently, this matrix drives the local connection of the index graph structure to recombine to form a perturbed topology.

[0027] Through the implementation of step 102, dynamically analyze the timing and spatial distribution characteristics of the terminal behavior, adjust the index path structure in real time, introduce high variability and non-repeatability, which can effectively prevent multiple access requests from falling into the same path, reduce the risks of path collision and enumeration attack, and improve path security and access isolation.

[0028] Step 103, inject a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel; In some examples, the quantum noise signal is a type of non-deterministic and high-entropy random signal generated based on the quantum uncertainty principle (such as the Heisenberg uncertainty principle), with characteristics such as unpredictability, non-replicability, and irreversibility. At the hardware layer, a quantum random number generator can be used to generate a true quantum random sequence, such as through photon scattering, electron tunneling effect, quantum decoherence, etc. Without quantum hardware support at the software layer, a high-entropy pseudo-random source can be used to simulate the characteristics of quantum noise, such as dynamic changes in the noise spectrum and non-periodic perturbations. The cross-layer verification channel refers to a multi-layer collaborative verification channel that runs through the physical layer of communication (such as link characteristics) and the logical layer (such as protocol tags, data structures). It is a cross-layer verification channel formed by embedding a quantum noise signal in the anti-collision verification path, which can achieve the joint verification of the authenticity and consistency of the data path.

[0029] Through the implementation of step 103, a quantum-level random noise signal is introduced, enabling the path verification process to have a dual perturbation mechanism at the physical layer and the logical layer, greatly increasing the difficulty for attackers to crack through side-channel analysis or protocol eavesdropping, and achieving the concealment, randomness, and anti-detection of the verification path.

[0030] Step 104, perform an asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result containing a blurred topological tag; In some examples, the target data index is the index information pointing to the specific data item of the data access instruction, which is the entry identifier during the data retrieval and retrieval process, and is a unique and structured positioning identifier, such as a hash address, B+ tree path, hash table key value, encrypted file handle, etc. The symmetric hash chain verification is a verification method that combines an asymmetric encryption mechanism (such as RSA / ECC) and a hash chain structure (such as a Merkle chain or Hash ladder). Through the sequential consistency of the chained hash and the uniqueness of the asymmetric signature, it ensures the integrity of the data path and the non-forgery of the identity. The blurred topological tag is a structural tag generated by blurring the real path information through perturbation, encryption, displacement, etc. in the index topological structure. These tags do not directly expose the real path, but can restore the verification result in the verification channel; the verification result is the final generated composite verification output, indicating that the index path is legally valid after multi-layer verification, and includes content such as hash chain signature, path consistency proof, and blurred tag matching degree.

[0031] Exemplarily, after the user requests the target data index of "contract.pdf" through the cross-layer verification channel, its current path node sequence is first constructed into a hash chain and signed by the client private key; the server side verifies the hash chain order and signature validity through the corresponding public key; meanwhile, the anti-collision verification path undergoes perturbation processing, and its obfuscation mark is matched and compared with the path structure in the verification channel, and finally a structured verification result including the path consistency proof and the obfuscation mark recognition degree is generated.

[0032] Through the implementation of step 104, combined with the asymmetric hash mechanism and the fuzzy topology mark, the forgery-proofness and immutability of data index verification can be ensured, while protecting the topology structure from being inferred or restored, enhancing the reliability and anti-inference ability of index verification, and realizing the dual guarantee of content authenticity and path privacy.

[0033] Step 105, perform dynamic remapping on the original index node according to the obfuscated topology mark, and output a secure data index resistant to parsing; In some examples, the original index node refers to the position node that directly maps the target data before topological perturbation and obfuscation processing, representing a real and resolvable data position reference; it can be generated by the index construction engine when the initial index topology is generated (such as step 101), and can be the primary key ID of a certain record in the database, the inode in the file system, the shard position identifier in the distributed storage, etc.; for example, the original index node corresponding to a certain file "contract.pdf" is IDnode_42, and the path is [root→doc→legal→node_42]. Dynamic remapping means remapping the original index node to a new node ID or path address at the logical or physical layer according to the obfuscated topology mark or the behavior perturbation result, preventing the static path from being exposed or reused. The perturbation factor (such as the perturbation hash value, path offset matrix) in the obfuscated topology mark can be used as the mapping seed, and a dynamic path transformation algorithm is applied, such as perturbation graph matching-based or random jump mapping table-based. The secure data index resistant to parsing is the final index result generated after performing dynamic remapping, with anti-parsing property, that is, external observers cannot easily restore the original data position it points to, ensuring the concealment and non-determinism of the access path and data position.

[0034] Exemplarily, after the verification in step 104 is completed, the mask and jump sequence used to perturb the path in the obfuscated topology mark are extracted, combined with the user terminal behavior perturbation factor, such as the trajectory change hash or touch rate, to calculate a dynamic perturbation matrix. This matrix is applied to the original index node mapping table, dynamically offsetting it to a new index address, and encrypted to form a "secure data index resistant to parsing". Finally, this index is submitted to the data scheduling module as a new access identifier to achieve real data retrieval.

[0035] Through the implementation of step 105, the re - encoding and obfuscation of the original node positions and access paths make the index structure dynamic and concealed. Even if intercepted, it is difficult to restore the true index path, effectively resisting attack behaviors such as structure parsing and reverse tracing, and ensuring the long - term security of the index.

[0036] Step 106, perform data access and storage based on the secure data index; In some examples, through the anti - parsing secure data index generated in the previous stage, locate and complete the read (query, download) or write (upload, modification) operations of the target data. Instead of directly relying on the real path or node ID, it uses the encrypted secure index and decoding mechanism for data interaction, thereby ensuring the concealment protection of the data position and index structure during transmission and retrieval.

[0037] Through the implementation of step 106, perform data access based on the secure index after multiple - layer verification and dynamic protection, ensuring that the data access process is both secure and reliable, and has traceability, anti - tampering ability and environmental adaptability, effectively realizing end - to - end data protection and access control.

[0038] In summary, the embodiment of this application uses the dynamic behavior fingerprint and network latency characteristics of the requesting terminal to construct a dynamic index structure, which can dynamically respond to different user environments, making it difficult for illegal terminals to reproduce the access patterns of legitimate terminals, effectively avoiding the problem that static structures are easily predicted or replicated; through the analysis of the spatio - temporal distribution characteristics of the dynamic behavior fingerprint, the index structure is adjusted in real - time, which can avoid different requests falling into the same index path, prevent the data index path from being collided or enumerated, and effectively defend against brute - force cracking and path speculation attacks; using quantum noise signals to disrupt the signal characteristics of the verification path and create uncertainty can make traditional electronic monitoring and side - channel analysis methods ineffective, enhancing the concealment and randomness of path verification; by constructing a verification mechanism for cross - network - layer channels, attackers cannot crack the system layer - by - layer, enhancing the hierarchical security of the system. Using an asymmetric hash chain can achieve data anti - tampering and request traceability, improving the consistency and credibility of access behaviors and index content; using fuzzy processing to cover up the real index structure can prevent attackers from reverse - inferring the original topology structure through the analysis of verification results, and further strengthening the protection mechanism through dynamic remapping to ensure that the index cannot be reverse - reconstructed even if intercepted. In summary, the data index secure access method provided by the embodiment of this application realizes the dynamicity, unpredictability and anti - parsing ability of the index path through the integration of multiple mechanisms such as dynamic behavior fingerprint, network characteristics, quantum noise, cross - layer verification and fuzzy remapping, and can comprehensively improve the security and anti - attack ability during the data access and storage process.

[0039] In some embodiments, generating a dynamic index topology structure that matches the current network latency jitter value based on the dynamic behavior fingerprint of the requesting terminal may include: obtaining the touch trajectory feature and network fluctuation feature of the requesting terminal within a preset time window; performing non-linear fusion on the touch trajectory feature and network fluctuation feature through a convolutional chaos algorithm to generate a topology generation factor; constructing a dynamic index topology structure in the complex domain space according to the topology generation factor, wherein the dimension parameter of the dynamic index topology structure is dynamically adjusted by the current network latency jitter value.

[0040] In some examples, the preset time window is the time range for sampling and statistics of terminal behavior data within a fixed time period (such as 1 second, 3 seconds, etc.), which can be set by the client behavior perception module. For example, a timer is started through the JavaScript front-end or mobile SDK, and the touch coordinates are collected every 100 ms. The touch trajectory feature refers to the feature information formed by the moving path of the user's finger on the screen, including direction, speed, bending degree, click point sequence, etc. The touchmove, touchstart, and touchend event data can be collected through the front-end to generate a trajectory point set, and then methods such as Bezier curve fitting and angle change detection are used to extract the features. The network fluctuation feature represents a set of parameters reflecting network stability and dynamics, such as latency change, jitter value, and packet loss rate within a unit time of the network. The convolutional chaos algorithm is an algorithm that forms a highly complex output by non-linearly mixing the input signals (behavior trajectory and network fluctuation), and fuses convolutional operations with chaotic mappings (such as Logistic, Tent, Henon Map) to increase the uncertainty and sensitivity of the output. For example, the touch trajectory feature [0.3, 0.5, 0.9] and network fluctuation [15, 22, 10] are used as the initial state input to the Logistic chaotic mapping after convolution, and the chaotic sequence [0.47, 0.81, 0.13] is output. The topology generation factor is a set of high-entropy parameters or vectors output by the convolutional chaos algorithm, which is used as the generation seed for constructing the index graph structure (node position, connection edge weight, hierarchical relationship, etc.), and can be a complex number array or a chaotic perturbation matrix. The dynamic index topology structure can be generated in the complex space (i.e., the mathematical space containing real and imaginary parts) using the topology generation factor. The complex domain can introduce structural changes such as phase and rotation to increase the perturbation dimension of the graph. The graph construction engine (such as graph neural network, graph chaos modeling system) can be used to arrange nodes in the complex coordinate space and set the edge connection relationship according to the complex weights to obtain the dynamic index topology structure. The complexity of the index structure, such as the number of node layers and the number of path branches, can be dynamically adjusted according to the current network jitter degree (i.e., the latency fluctuation range). The greater the jitter, the more complex the structure. For example, if the network jitter < 0 ms, the topology dimension maintains the base value, such as a 3-order tree; if the jitter > 50 ms, it increases to a high-order multi-branch graph structure.

[0041] Exemplarily, in a user data access request, the terminal performs a series of operations such as a light swipe and a quick tap within a 3-second time window. At the same time, the network fluctuation detection module records that the current dynamic index topology structure is 32 ms. Then, the trajectory features [0.3, 0.6, 0.2] and the fluctuation features [15, 20, 18] are extracted and input into the convolutional chaos algorithm to obtain topology generation factors such as [0.78 + 0.1i, 0.43 - 0.2i]. Subsequently, nodes are arranged in the complex domain graph space to construct a dynamic index topology graph, and the topology is adjusted to a 4-layer bifurcation structure according to the jitter value. This graph becomes the basis for generating the access index, and the subsequent process continues to perform reconstruction, verification, and scrambling processing based on this structure.

[0042] Through the implementation of the above embodiments, terminal features such as touch trajectories and network fluctuations are introduced, and a chaotic algorithm and the complex domain are used to construct an index topology, making the generated index structure highly individualized and dynamic, which can significantly reduce the risk of the index path being predicted, copied, or reproduced, and break the security risks brought by the static structure from the source.

[0043] In some embodiments, the foregoing step 102 may include: decomposing the foregoing spatio-temporal distribution features into sequence fluctuation features in the time dimension and trajectory distribution features in the space dimension; calculating a dynamic correlation matrix according to the entropy value of the sequence fluctuation features and the variance of the trajectory distribution features; adjusting the connection weights of the dynamic index topology structure in real time based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; screening the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retaining the paths that meet the preset anti-conflict conditions as anti-collision verification paths, where the anti-conflict conditions may include that the path conflict probability is less than a first preset threshold and the path jump count is greater than a second preset threshold.

[0044] In some examples, the sequence fluctuation feature describes the distribution and rhythm change features of user behavior events (such as clicks and swipes) on the time axis, reflecting the timing law of behavior. The behavior events (such as touch timestamps) can be converted into time series (such as click intervals and swipe rates), and its entropy, variance or FFT spectrum can be calculated to obtain the sequence fluctuation feature. The trajectory distribution feature describes the distribution pattern of the user operation trajectory (such as finger swipe) in space, including the distribution range, path density, trajectory angle distribution, etc. The position distribution can be extracted from the set of touch point coordinates and quantified with statistical indicators (such as standard deviation, variance, and centroid offset) to obtain the trajectory distribution feature. The dynamic correlation matrix is a matrix structure used to describe the similarity or correlation strength between spatio-temporal behavior features. The sequence entropy value and trajectory variance can be input into a correlation calculation model (such as cosine similarity, inverse Euclidean distance, mutual information, etc.) to generate a dynamic correlation matrix. The preset anti-collision algorithm is an algorithm used to identify and screen paths with a low probability of path overlap and high jump features, ensuring path diversity and security. The preset anti-collision algorithm can be implemented as a path scoring algorithm, setting a conflict probability function and a jump statistics mechanism, combined with historical access frequency and graph structure perturbation factors. The preset anti-conflict condition is a judgment criterion for screening paths, which can ensure that the selected paths have a low collision rate and high variability under the current structure. It can be set through empirical values and the results of adversarial simulation training. For example, the conflict probability < 20% and the number of jumps > 3. The first preset threshold is used to limit the maximum value of the path conflict probability (such as the possibility of a path being shared by multiple users). Generally, the smaller it is, the safer it is. It can be set according to access logs and simulated attack analysis. For example, setting it to 0.05 means that the probability of the path being requested simultaneously must be less than 5%. The second preset threshold is used to limit the minimum value of the path jump count to ensure the variability and unpredictability of the path. It can be set according to the average jump ability of the topology graph.

[0045] Exemplarily, in a data access, the click and swipe operations of the user within a 3-second time window are recorded. The behavior entropy in the time dimension is extracted as 0.86, and the variance of the spatial trajectory distribution is 0.72. The calculated dynamic correlation matrix shows that the user behavior has a medium correlation level in space and time, and the connection probability between the middle-level nodes in the driving index graph is perturbed and weighted. Subsequently, conflict analysis and jump statistics are performed on all paths in the reconstructed topology, and paths that meet the conditions of "conflict probability < 0.05 and number of jumps ≥ 4" are selected as anti-collision verification paths, which are finally used in the index verification stage.

[0046] Through the implementation of the above embodiments, the dynamic behavior features are refined into two dimensions of time and space, and mathematical models such as entropy value and variance are introduced for path reconstruction, which can effectively improve the randomness and anti-collision ability of the path, avoid multiple requests falling into the same path, significantly reduce the probability of path conflict and data collision, and improve the index stability and attack defense ability.

[0047] In some embodiments, the foregoing step 103 may include: obtaining path parameters of the anti-collision verification path, where the path parameters may include path length, node distribution density, and hopping frequency; generating a quantum noise signal that matches the path parameters based on a quantum random number generator, where the spectral width of the quantum noise signal is positively correlated with the hopping frequency; performing a convolution process on the quantum noise signal and the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; injecting the phase perturbation factor into the boundary between the physical layer and the logical layer of the anti-collision verification path to form a cross-layer verification channel, where the verification data packet of the cross-layer verification channel carries a noise fingerprint in the physical layer and embeds a dynamic hash tag in the logical layer.

[0048] In some examples, the path parameters are physical and logical feature quantization indexes of the anti-collision verification path. In addition to path length (such as the total number of hopping nodes), node distribution density (the number of nodes per unit path segment), and hopping frequency (the number of times the path structure changes per unit time), derivative parameters such as path entropy value and node similarity may also be included. The spectral width of the quantum noise signal needs to be dynamically adapted to the hopping frequency to achieve signal-path coupling. For example, when the hopping frequency is 2 Hz, a broadband noise signal with a spectral width of (hopping frequency × 1 kHz) is generated through a quantum random number generator (such as a quantum entropy source based on an avalanche diode) to form a time-frequency domain dynamic coverage ability. The chaotic modulation algorithm may adopt a Logistic-Tent composite mapping model to perform a non-linear convolution on the quantum noise signal and the path topological parameters (such as node connection matrix, edge weight distribution) to generate a perturbation factor with phase rotation characteristics (such as phase offset angle θ = 0.78π ± Δ), which can dynamically change the carrier phase of signal transmission and the generation reference of the hash tag in the logical layer. The verification data packet of the cross-layer verification channel superimposes a quantum noise fingerprint on the physical layer through orthogonal frequency division multiplexing subcarriers, manifested as random dispersion of the signal constellation diagram; in the logical layer, it drives the SHA3-512 hash iteration through the perturbation factor to generate a dynamic tag carrying a timestamp and behavioral entropy, realizing physical-logical double concealment.

[0049] Exemplarily, assume that the path length of the anti-collision verification path is 5 hops, the node distribution density is 3 nodes / hop, the hopping frequency is 3 Hz, and the quantum random number generator generates a quantum noise signal with a spectral width of 3 kHz (frequency band 2.5 - 5.5 kHz). The noise signal and the path connection matrix are modulated through a Logistic chaotic map (μ = 3.99, x0 = 0.42) to generate a phase perturbation factor θ = [0.21π, 0.67π, 1.32π]. At the physical layer, θ is injected into the phase compensation module of the orthogonal frequency division multiplexing pilot subcarrier to make the channel estimation value deviate from the true value by ±θ; at the logical layer, θ is used as the control parameter for the hash iteration rounds to generate a dynamic tag "9f86d08...3b9d5". After the attacker intercepts the data packet, the channel characteristics are distorted due to quantum noise at the physical layer, and the dynamic hash at the logical layer cannot match the historical pattern, ultimately triggering a verification failure.

[0050] Through the implementation of the above embodiments, the quantum noise signal is dynamically matched with the path parameters, and phase perturbation is injected into the physical and logical layers to construct a "cross-layer" verification mechanism, which can effectively defend against side-channel attacks, traffic analysis, and single-layer cracking methods, making the verification process highly concealed and unpredictable, and enhancing the multi-layer security protection depth of the system.

[0051] In some embodiments, the foregoing step 104 may include: constructing an asymmetric hash chain structure based on the dynamic hash tag and the phase perturbation factor, wherein the public key of the asymmetric hash chain structure is generated from the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated from the expiration timestamp of the dynamic hash tag of the logical layer; cross-verifying the node attributes of the target data index to generate an initial verification result; performing iterative signature on the initial verification result using the asymmetric hash chain structure to obtain a multi-level verification signature; comparing the multi-level verification signature with a preset anti-interference threshold, and if the threshold condition is met, extracting the topological distribution characteristics of the multi-level verification signature; performing fuzzy coding on the topological distribution characteristics based on the random mask factor in the quantum noise signal to generate a fuzzy topological tag, wherein the generation process of the fuzzy topological tag is dynamically associated with the dimension parameters of the dynamic index topological structure.

[0052] In some examples, the dynamic hash tag is a time-varying identifier generated by the logic layer to verify data packets, which can include a combined hash value of a behavioral entropy hash (such as SHA3-512(user click rhythm + swipe trajectory)) and a timestamp salt value (such as UTC milliseconds truncated to hexadecimal). Its expiration timestamp is dynamically set through a preset time-to-live (TTL) policy. For example, it is valid within 10 seconds after the logic tag is generated. The phase perturbation factor, as a chaotic modulation parameter, participates in the generation of the initial vector for hash iteration during the construction of the asymmetric hash chain. For example, the hash round offset is adjusted through the θ value. Cross-verification refers to the multi-dimensional comparison of the node attributes of the target data index, such as node ID hash, storage location metadata, and access permission labels, including logical consistency (such as the index path matching the user permissions) and physical consistency (such as whether the actual response delay of the storage node is consistent with the topology prediction value). If the hash chain signature in the logic layer matches the public key decryption result of the physical layer noise fingerprint, the initial verification result is generated as "trusted". The multi-level verification signature is generated by nesting chained hashing and asymmetric signatures. For example, the primary signature is signed by the client private key for the initial verification result, and the secondary signature is signed by the server private key for the primary signature result to form a hierarchical verification chain. The preset anti-interference threshold is modeled based on historical attack data and set as the lower limit of the confidence score for multi-level signatures. For example, the primary signature similarity > 95% and the secondary signature delay fluctuation < 5ms. The threshold conditions can be adjusted through a dynamic policy engine. The random mask factor is a random number sequence extracted from quantum noise signals, such as intercepting a 32-bit segment of a quantum random source, which is used for exclusive OR masking and displacement obfuscation of topological distribution features, such as node connectivity and path weight distribution. Its masking strength is associated with the dimensional parameters of the dynamic index topology (such as the number of node layers N). For example, when N = 4, the mask is circularly shifted left by (4 mod 8) = 4 bits.

[0053] Through the implementation of the above embodiments, the public and private keys of the asymmetric hash chain are generated separately in the physical layer and the logic layer. A strong verification chain is constructed through dynamic hashing and multi-level signatures, and fuzzy coding is achieved by combining quantum noise, enabling the data index verification to have triple guarantees of anti-tampering, traceability, and anti-analysis, ensuring index consistency, verification authenticity, and structural privacy.

[0054] In some embodiments, the foregoing step 105 may include: parsing the quantum noise spectrum characteristics and topological distribution parameters in the obfuscated topology tag to generate an anti-parsing mapping rule; dynamically offsetting the storage location and access permission label of the original index node based on the anti-parsing mapping rule to generate an implicit mapping relationship; performing non-linear confusion on the implicit mapping relationship according to the dimension parameter of the dynamic index topology structure and the current system timestamp to generate a randomized index mapping table; injecting a dynamic confusion factor into the randomized index mapping table to generate a secure data index resistant to parsing, wherein the generation frequency of the dynamic confusion factor is synchronously updated with the hopping frequency of the quantum noise signal.

[0055] In some examples, the anti-parsing mapping rule refers to a set of rules that can map the real address or identifier of the original index node to a virtual node that cannot be directly parsed through perturbation transformation; the quantum noise spectrum characteristics (such as bandwidth, modulation mode) and topological distribution parameters (such as node offset matrix, connection chaos index) included in the obfuscated topology tag can be parsed and used as input parameters of the perturbation function to obtain the anti-parsing mapping rule. The implicit mapping relationship refers to a non-explicit representation and non-directly reverse-derivable logical correspondence between the original index node and its perturbed node. Based on the anti-parsing mapping rule, attributes such as the physical address, path label, and access permission of the original node can be perturbed and offset without retaining a reversible path. The randomized index mapping table is a table structure that remaps the original index node to a set of time-varying and unpredictable index addresses after multi-dimensional perturbation. It is a transitional implicit data structure. The topological dimension parameter (such as the number of graph layers, node fan-out rate) and the system timestamp (such as UNIX time, UTC nanosecond level) can be used as seeds of the confusion function to generate table entries for disturbing the node ID or path logic. The dynamic confusion factor is a set of time-sensitive perturbation parameters injected into the index mapping table to enhance the unpredictability and anti-reconstruction ability of the index path. The update frequency of the dynamic confusion factor is synchronized with the hopping frequency of the quantum noise signal and can be generated in real time based on the quantum entropy source or simulated by a pseudo-quantum random number generator to generate high-frequency perturbations.

[0056] Exemplarily, during a security data index generation process, first, the perturbation spectrum [8Hz, 12Hz] and the offset parameters [Δx = 3, θ = π / 6] are parsed from the fuzzified topology tags to generate an anti-parsing mapping rule; subsequently, according to this rule, the true position and access label of node IDnode_42 are perturbed and offset to generate an implicit mapping {IDnode_42 → IDδ_Y}; a non-linear confusion path is constructed based on the current network jitter dimension parameter of 4 layers and the system timestamp 1725817223 to form a randomized index mapping table; a confusion factor σ(t) dynamically generated by the quantum noise spectrum is injected into this structure every 100ms, and finally, a security data index S_index(IDδ_Y) with anti-parsing ability is output.

[0057] Through the implementation of the above embodiments, an anti-parsing mapping rule is generated by parsing the fuzzified tags, and a dynamic confusion mechanism is introduced to disrupt the direct relationship between the original index and the access permission, thereby constructing an implicit index path that is irreversible and difficult to restore, which can significantly improve the reverse reasoning difficulty of the index layer and the overall anti-parsing ability.

[0058] In some embodiments, the foregoing method for secure access to data indexes may further include: when an abnormal parsing behavior for the security data index is detected, a virtual decoy index set is generated according to the distribution characteristics of the fuzzified topology tags, and the virtual decoy index set is overlaid on the original index nodes; the topology parameters of the virtual decoy index set are dynamically adjusted based on the jump frequency of the quantum noise signal to generate a confusion index network with an attack traceability function; when a virtual node in the confusion index network is activated for access, the behavior fingerprint characteristics of the attacker are extracted and the data self-destruction instruction of the distributed storage node is triggered.

[0059] In some examples, abnormal parsing behavior refers to the act of attempting to access, reconstruct, scan, or reverse-engineer a secure data index structure in an unauthorized and non-standard way. It is usually accompanied by characteristics such as abnormal parsing frequency, failed path reconstruction, and inconsistent jump patterns. The abnormal behavior characteristics (such as repeatedly parsing the same node, abnormal path jump sequence, access rate exceeding the normal threshold, etc.) can be detected by analyzing the index access logs through the behavior monitoring module. The virtual decoy index set refers to a set of pseudo-index nodes that are constructed, logically existent but physically without real data binding, and are used to mislead the attacker's parsing logic. With the distribution characteristics of topological markers (such as node dense areas, frequently accessed paths) blurred as a reference, virtual nodes are generated to cover the original nodes or be embedded into the topological structure. The obfuscated index network is a dynamic virtual topological network composed of decoy nodes, deformed paths, perturbed connection relationships, etc., and is used to mislead attackers, trap parsing behaviors, and conduct behavior traceability. The behavioral fingerprint characteristics of an attacker refer to the set of behavioral trace characteristics exposed when accessing the decoy index, including request patterns, path selection strategies, time rhythms, terminal identifiers, etc. The behavioral trajectories can be collected and feature vectors can be extracted through system behavior recorders (such as front-end JS SDK, border gateway logs, distributed link tracing); for example, if the access path preference is breadth-first, the jump rate is lower than 0.2, and the UA string is an automated script tool, then this behavioral fingerprint can be marked as a potential Bot-type attack fingerprint. The data self-destruction instruction is an operation instruction sent to distributed data nodes for forced destruction, off-chain, encrypted overwrite, etc., and is used to actively destroy data to prevent leakage when threatened. When the obfuscated index node is activated (i.e., the decoy is accessed) and the attack fingerprint is confirmed to be valid, the central control node broadcasts a signed self-destruction instruction to the data nodes.

[0060] Exemplarily, during an index access process, it is detected that the IP address 203.0.113.78 recursively accesses multiple index paths quickly within a short period of time. The access behavior exhibits path scanning characteristics and disguised UA information. After being identified as abnormal parsing behavior, a virtual decoy index set containing 32 nodes is immediately generated according to the fuzzy topological characteristics of the hot nodes, and some of the node structures are overlaid on the target real node paths to form an obfuscated index network; this decoy network dynamically generates an irregular multi-layer path structure through the current quantum noise signal jump frequency of 13 Hz. When the attacker accesses node_fakeA, it triggers the extraction of behavioral fingerprints. After recording its jump pattern, trigger timestamp, and UA characteristics, it is identified as a medium-high risk attack behavior; subsequently, a signed self-destruction instruction is sent to the involved real data nodes to perform data overwrite and off-chain operations to ensure that sensitive data cannot be recovered in the attacker's access path.

[0061] Through the implementation of the above embodiments, a decoy index network and an attack traceability mechanism are introduced. When malicious behavior is detected, the virtual node and data self-destruction process are triggered, which has the capabilities of automatic countermeasure, traceability positioning, and dynamic response, and can enhance the system's perception and defense capabilities against active attacks.

[0062] In some embodiments, the foregoing preset anti-collision algorithm may include: traversing all candidate paths of the reconstructed topology network, calculating the collision probability and the number of hops of each path; if the collision probability of the candidate path is less than the first preset threshold and the number of hops is greater than the second preset threshold, it is marked as a valid anti-collision path; according to the topological distance and network load parameters of the valid anti-collision path, priority sorting is performed to generate the final set of anti-collision verification paths.

[0063] In some examples, the calculation method of the collision probability is as follows: based on the historical access logs, the frequencies of each path being requested are statistically analyzed. For example, path A was accessed 100 times in the past 1 hour. Combining with the path structure similarity, such as the node sequence coincidence rate and the jump direction consistency, the Bayesian conditional probability model is used to calculate the collision probability P_collision = P(path being reused | structure similarity > 0.7). The statistical rule of the number of hops is: when traversing the path node sequence, if the hash value difference of adjacent node IDs exceeds the preset threshold (such as the Hamming distance between SHA256(node_i) and SHA256(node_j) > 128bit), it is counted as a valid hop once. The priority sorting algorithm adopts a multi-objective optimization model. The topological distance (such as the number of hops) is normalized into a delay cost factor α = number of hops × single-hop reference delay (such as 5ms / hop), and the network load parameter is converted into a bandwidth occupancy weight β = 1 / (current link utilization + ε). The final priority score Score = (1 / α) × β × log(number of hops). The path with a higher score is preferentially selected; the first preset threshold can be set to the collision probability P_collision < 0.1, that is, the path reuse probability is less than 10%, and the second preset threshold is set to the number of hops ≥ 5 times.

[0064] Through the implementation of the above embodiments, through the priority decision-making mechanism of quantifying the collision probability and dynamic load perception, it is possible to optimize the path selection efficiency while ensuring a low collision risk, achieving a balance between security and performance; the multi-dimensional threshold control enables the path screening to have environmental self-adaptability, and it is difficult for attackers to break through the verification logic through a fixed pattern, significantly enhancing the anti-enumeration and anti-replay capabilities of the index path.

[0065] In some embodiments, the foregoing method for generating a random perturbation factor may include: generating a chaotic perturbation sequence based on the current system timestamp and the singular value decomposition result of the dynamic correlation matrix; performing convolution mixing on the chaotic perturbation sequence and a preset white noise signal to generate a random perturbation factor.

[0066] In some examples, the method for generating a chaotic perturbation sequence is as follows: Convert the current system timestamp, such as the UNIX timestamp 1620000000.123456, into a floating-point seed value. For example, take the fractional part 0.123456 as the initial value x0. The dynamic correlation matrix is decomposed by singular value decomposition (SVD) to obtain the singular value vector [σ1, σ2, σ3], and after normalizing it, it is used as the chaotic mapping parameter. For example, the improved Logistic chaotic system is adopted: x n+1 = μ·σ k ·x n (1 - x n ), where μ = 3.999 and σ k is the singular value component selected cyclically; the preset white noise signal is generated by a hardware true random number generator and convolved with the chaotic sequence in the time-frequency domain: After performing Fourier transforms on the two signals in the frequency domain and then multiplying them point by point, and then inverse-transforming back to the time domain to generate a mixed perturbation factor. The finally generated random perturbation factor needs to satisfy that the Lyapunov exponent > 0.5 (strong chaotic characteristic) and the autocorrelation coefficient < 0.1 (low periodicity).

[0067] Exemplarily, assume that the SVD decomposition of the dynamic correlation matrix gives σ = [0.92, 0.35, 0.18], the system timestamp is 1620000000.123456, and the initial x0 = 0.123456; for the first round of iteration, take σ k = 0.92, and calculate x1 = 3.999×0.92×0.123456×(1 - 0.123456) = 0.415; after 100 times of cyclic iteration, a chaotic sequence [0.415, 0.732, 0.218,...] is generated. The white noise signal segment is [0.312, -0.087, 0.654,...], and after convolution, a random perturbation factor [0.123×0.312, 0.732×(-0.087), 0.218×0.654,...] = [0.038, -0.064, 0.142,...] is generated. If an attacker attempts reverse derivation, due to the sensitivity of the chaotic initial value (δx0 = 1e - 6 resulting in an error > 90% after a hundred steps), the original parameters cannot be restored.

[0068] Through the implementation of the above embodiments, combining the system time entropy source and the mathematical perturbation of matrix decomposition, a chaotic sequence with irreversible characteristics is generated, and then the randomness is enhanced by physical-level white noise, making the perturbation factor have both algorithmic complexity and physical unclonability, effectively resisting reverse attacks based on model inference.

[0069] In some embodiments, the foregoing chaotic modulation algorithm may include: inputting a quantum noise signal and topological parameters into a preset chaotic system to generate a chaotic modulation sequence; performing segmented weighting on the chaotic modulation sequence according to the node distribution density of the anti-collision verification path to generate a phase perturbation factor, wherein the initial parameters of the chaotic system are dynamically set by the spatio-temporal distribution characteristics of the dynamic behavior fingerprint.

[0070] In some examples, the preset chaotic system adopts a three-dimensional Lorenz system, and its differential equation parameters (σ, ρ, β) are dynamically set by the spatio-temporal distribution characteristics of the dynamic behavior fingerprint: σ = 10×(spatio-temporal trajectory variance)+5, ρ = 28×(time series entropy), β = 8 / 3×(spatial distribution kurtosis). After being sampled by an ADC, the quantum noise signal is converted into a digital sequence Q = [q1, q2,..., q n , and the topological parameters include a node connection matrix C and an edge weight vector W. The chaotic modulation process is as follows: Input Q and C into the Lorenz system, iteratively generate three-dimensional chaotic trajectory points (x, y, z), and take the z component to form a chaotic modulation sequence. The segmented weighting strategy is set according to the node distribution density d (number of nodes / unit path length): when d>3, apply a Hanning window weighting to the chaotic sequence; when d≤3, use exponential decay weighting, and finally generate a phase perturbation factor θ = arctan(Σ(weighted chaotic sequence)) with path adaptation characteristics.

[0071] Exemplarily, for an anti-collision verification path with a node distribution density d = 4, a spatio-temporal variance of the dynamic behavior fingerprint of 0.7, a time entropy of 0.9, and a spatial kurtosis of 2.1, calculate the Lorenz parameters σ = 10×0.7 + 5 = 12, ρ = 28×0.9 = 25.2, β = 8 / 3×2.1 = 5.6. The quantum noise signal Q = [0.12, -0.33, 0.78,...] and the connection matrix C are input into the Lorenz system, and the z sequence [0.45, -0.12, 0.67,...] is iteratively generated. Since d = 4>3, apply a Hanning window weighting to get [0.45×0.08, -0.12×0.25, 0.67×0.45,...], and calculate the phase perturbation θ = arctan(0.036 - 0.03 + 0.302) = 0.98π. After injecting this factor into the pilot phase of the physical layer, the constellation diagram presents an asymmetric vortex shape; the logical layer hash mark generates "c4ca42...b67" due to the θ offset, and attackers cannot crack it through a fixed phase template.

[0072] Through the implementation of the above embodiments, through the dynamic configuration of chaotic parameters driven by behavior fingerprints, the modulation process is deeply bound to the terminal characteristics. The segmented weighting mechanism enables the phase perturbation factor to have path topology self-adaptability, creates channel feature confusion in the physical layer, and strengthens the unpredictability of the hash in the logical layer, forming a cross-layer collaborative defense system.

[0073] In some embodiments, the method for constructing the verification data packet of the foregoing cross-layer verification channel may include: adding a phase offset amount based on a quantum noise signal to the original data packet at the physical layer to generate a noise fingerprint data frame; performing asymmetric encryption on the noise fingerprint data frame at the logical layer and embedding a dynamic hash tag based on the current timestamp; wherein, the generation key of the dynamic hash tag and the expiration time of the encryption index key are synchronously updated.

[0074] In some examples, the method for adding the physical layer phase offset is as follows: generating a phase noise sequence θ∈[0,2π) that follows a uniform distribution through a quantum random number generator (QRNG), and injecting θ into the phase compensation module of the pilot subcarrier by using orthogonal frequency division multiplexing (OFDM) technology. In a specific implementation, N_pilot pilot subcarriers are inserted into each OFDM symbol, and a phase perturbation of θ_i = QRNG() mod 2π is applied to each pilot point to generate a noise fingerprint data frame. Asymmetric encryption at the logical layer uses elliptic curve encryption (ECC), the public key Pub_Key is preset by the system center, and the private key Pri_Key = KDF(expiration timestamp || quantum noise entropy), where KDF is a key derivation function based on SHA-3. The generation formula of the dynamic hash tag is Hash_Marker = SHA3-512(UTC timestamp || θ_i sequence || data packet payload), and the effective period of this tag is strictly synchronized with the TTL (time to live) of Pri_Key. For example, the key pair is updated every 30 seconds, and the expired tag automatically becomes invalid.

[0075] Exemplarily, when a user requests to access an encrypted file, the physical layer QRNG generates θ = [0.5π, 1.2π, 2.7π], and corresponding phase offsets are applied to the pilot positions 1, 5, and 9 subcarriers of the OFDM symbol respectively. After the data frame is transmitted through the channel, the receiving end detects that the phase distortion of the pilot points is [0.52π, 1.18π, 2.65π], and the difference from the original θ is used for noise fingerprint verification. The logical layer uses the expiration timestamp 2023-09-20T14:30:00Z to generate Pri_Key = KDF(1621596600 || QRNG_entropy), and for Hash_Marker = SHA3-512("1621596600_0.5π_1.2π_2.7π_ <payload>") performs ECC signature. If the attacker attempts to reuse the old key at 14:30:15, the decryption will be rejected due to the expiration of the TTL.

[0076] Through the implementation of the above embodiments, the deep coupling of physical-layer quantum noise injection and logical-layer time-sensitive keys enables the verification packet to have both channel feature concealment and key dynamics. The attacker can neither restore the phase perturbation pattern through physical-layer signal analysis nor break through the logical-layer verification by replaying the old hash tag, achieving cross-layer collaborative protection.

[0077] In some embodiments, the generation method of the foregoing random mask factor may include: extracting the energy distribution parameters of the quantum noise signal in a preset frequency band; generating a chaotic mask sequence according to the energy distribution parameters and the connection weights of the dynamic index topology; and performing a normalization process on the chaotic mask sequence to obtain the random mask factor.

[0078] In some examples, the extraction method of the energy distribution parameters is: performing a short-time Fourier transform (STFT) on the quantum noise signal, selecting a preset frequency band (such as 3 kHz - 5 kHz) to calculate the energy integral E = Σ|X(f)|²Δf, and normalizing it to the energy ratio P = E / Σ full-band energy. The generation of the chaotic mask sequence uses an improved Henon map: x n+1 = 1 - α·P·x n ² + y n , y n+1 = β·C_ij·x n , where C_ij is the connection weight from node i to j in the dynamic index topology, and α = 1.4, β = 0.3 are chaotic parameters. The normalization process linearly transforms the iteratively generated x sequence to the integer range of [0, 255], and truncates it by bytes to generate the mask factor Mask = floor(255*(x n - x_min) / (x_max - x_min)).

[0079] Through the implementation of the above embodiments, the spectral characteristics of quantum noise are dynamically bound to the topological connection relationship, and a high-entropy mask sequence is generated through a chaotic system; the energy-aware chaotic parameter adjustment mechanism enables the mask factor to have environmental self-adaptability, and the normalization process ensures compatibility with the data format, effectively resisting mask cracking attacks based on statistical analysis.

[0080] In some embodiments, the foregoing cross-verification may include: traversing the nodes of the target data index, calculating the mapping relationship between the attribute hash value of each node and the dynamic index topology; if the deviation between the node attribute hash value and the mapping relationship exceeds the third preset threshold, marking it as an abnormal node and triggering the path backtracking mechanism; and re-executing the asymmetric hash chain verification after removing the abnormal nodes.

[0081] In some examples, the method for calculating the cross-validation node attribute hash value is as follows: perform SHA-256 hashing on the storage path, access permission label, and physical location metadata of the node to generate a 64-byte hash value H_node. The mapping relationship of the dynamic index topology is established through the chaotic correlation matrix C, and the formula for calculating the node hash deviation degree is . The third preset threshold is set to 0.1 (that is, a hash deviation exceeding 10% is regarded as abnormal). After the path backtracking mechanism is triggered, the system traverses the last 5 nodes in reverse along the verification path, recalculates their hash chain signatures, and updates the topology structure.

[0082] Exemplarily, the hash value of a certain node , after being calculated by the chaotic matrix C, is , and the deviation degree , which is marked as an abnormal node. The system backtracks to the previous node ID_25, regenerates the hash chain, constructs a new topology, and after removing the ID_42 node, the verification pass rate is increased to 99.7%.

[0083] Through the implementation of the above embodiments, through quantitative hash deviation detection and dynamic path backtracking, real-time credibility verification of index nodes is achieved, effectively defending against node tampering and path hijacking attacks, and ensuring the integrity and consistency of the verification chain.

[0084] In some embodiments, the method for generating the foregoing anti-analytical mapping rule may include: extracting the energy peak distribution of the quantum noise spectrum feature in a preset frequency band; constructing a complex-domain anti-analytical weight matrix according to the energy peak distribution; inputting the topology distribution parameter into a preset anti-analytical function to generate an initial mapping rule; and performing weighted correction on the initial mapping rule through the anti-analytical weight matrix to generate a final anti-analytical mapping rule.

[0085] In some examples, the method for extracting the energy peak distribution is as follows: perform power spectral density (PSD) analysis on the quantum noise signal, and detect the first 3 main peak frequencies f1, f2, f3 and their amplitudes A1, A2, A3 in a preset frequency band (such as 3 kHz - 5 kHz). The formula for constructing the complex-domain anti-analytical weight matrix W is W_ij = A_k·e^(j2πf_kτ_ij), where τ_ij is the topological jump delay from node i to j. The anti-analytical function adopts the Fourier perturbation model F(x)=FFT(x)·W, the initial mapping rule R_init is generated from the node connection degree and weight, and the final rule R_final = Normalize(R_init ⊙ F(R_init)), where ⊙ is the Hadamard product.

[0086] Exemplarily, the main peaks of quantum noise are detected as f1 = 3.5 kHz (A1 = 0.8), f2 = 4.1 kHz (A2 = 0.6), f3 = 4.7 kHz (A3 = 0.4), and the node hopping delay τ_12 = 5 ms. Construct W_12 = 0.8e^(j2π×3500×0.005) = 0.8e^(j35π) = 0.8(-1 + 0j) = -0.8. The initial rule R_init = [0.3, 0.7, 0.5], and after the F(x) transformation, R_final = [0.3×(-0.8), 0.7×0.6, 0.5×0.4] = [-0.24, 0.42, 0.2], which is normalized to [0, 0.66, 0.34]. An attacker cannot reverse-engineer the original topological parameters by observing the final rule.

[0087] Through the implementation of the above embodiments, the spectral characteristics of quantum noise are encoded into a complex weight matrix, and the nonlinearity and irreversibility of the mapping rule are enhanced through frequency-domain transformation, enabling the anti-analysis rule to have both physical noise binding and mathematical chaos characteristics, significantly increasing the difficulty of reverse engineering of the index path.

[0088] In some embodiments, the method for generating the foregoing dynamic confusion factor may include: calculating the confusion factor update period based on the hopping frequency of the quantum noise signal; within each update period, generating a chaotic sequence according to the hash value of the current index mapping table and the system timestamp; and performing normalization truncation on the chaotic sequence to obtain the dynamic confusion factor.

[0089] In some examples, the confusion factor update period T = 1 / (2×hopping frequency). For example, if the hopping frequency f = 10 Hz, then T = 50 ms. The chaotic sequence generation uses a Logistic-Tent hybrid model: x n+1 = (μ×x n (1 - x n ) + λ×(1 - 2|x n - 0.5|)) mod 1, where μ = 3.99, λ = 0.7, and the initial value x0 = SHA3(current mapping table hash || timestamp) mod 1. The normalization truncation maps x n to an integer in [0, 255]: Mask = floor(255×x n ).

[0090] Exemplarily, the jump frequency is 15 Hz → T ≈ 33 ms, the current hash = 0x5a3d, the timestamp is 1620000000, calculate the last 8 bytes of x0 = SHA3("5a3d1620000000") → 0.723. Iterate the Logistic-Tent to get x1 = 0.632, x2 = 0.417..., take x5 = 0.891 → Mask = floor(255 × 0.891) = 227 (0xE3). The data byte 0x6A is obfuscated as 0x6A ⊕ 0xE3 = 0x89, and the attacker cannot infer the original value from 0x89.

[0091] Through the implementation of the above embodiments, the obfuscation rhythm is dynamically adjusted based on the noise jump frequency, and combined with the strong randomness of the cryptographic hash and the chaotic model, it is ensured that the obfuscation factor has time-varying and unpredictable properties, effectively resisting replay attacks and pattern analysis.

[0092] In some embodiments, the method for executing the foregoing data self-destruction instruction may include: locating the associated distributed storage node according to the behavior fingerprint feature of the attacker; sending an encrypted instruction carrying the self-destruction key to the storage node, and the self-destruction key is generated by the dimension parameter of the dynamic index topology structure; after the storage node verifies the validity of the self-destruction key, it performs multiple overwrites and erasures on the target data block, and destroys the corresponding index mapping record.

[0093] In some examples, the self-destruction key generation formula is K_selfdestruct = SHA3(dimension parameter d || time window ID), where d is the number of topological layers, and time window ID = floor(current time / self-destruction period). The encrypted instruction uses the AES-GCM mode, and the additional authentication tag TAG = HMAC(K_selfdestruct, node ID). The data overwrite adopts the DoD 5220.22-M standard and performs 3 random pattern overwrites (0x00 → 0xFF → random code).

[0094] Exemplarily, the dimension parameter d = 4, the time window ID = 1620000000 / 300 = 5400000, generate K_selfdestruct = SHA3("45400000") = 0x8d3a...c9f. The TAG of node ID = 192.168.1.42 is HMAC(0x8d3a, "192.168.1.42") = 0x7e5f...a9. After the node verifies that the TAG is valid, it performs an overwrite on the data block: the first round writes all 0s, the second round writes all 1s, the third round fills with random numbers, and finally deletes the index record.

[0095] Through the implementation of the above embodiments, the self-destruction key bound by topological parameters and the military-grade erasure standard ensure the rapid and irreversible destruction of sensitive data in the event of an intrusion, while preventing key reuse attacks, achieving the immediacy and thoroughness of attack response.

[0096] In some embodiments, the aforementioned step 106 may include: parsing the randomized index mapping table in the aforementioned security data index, and extracting the dynamic confusion factor and the implicit mapping relationship; generating a dynamic decryption key based on the implicit mapping relationship, where the dynamic decryption key is jointly generated by the dynamic confusion factor and the hash value of the current system timestamp; locating the target data block according to the node distribution characteristics of the randomized index mapping table, and performing hierarchical decryption on the target data block through the dynamic decryption key to obtain the initial plaintext data; performing a secondary integrity verification on the initial plaintext data, and the secondary integrity verification may include verifying the consistency between the Fourier phase perturbation characteristics of the target data block and the spectral characteristics of the quantum noise signal; if the verification passes, perform deconfusion processing on the decrypted data, generate the final plaintext data and return it to the requesting terminal, and at the same time update the dimension parameters of the dynamic index topology structure to trigger the index reconstruction of the next cycle.

[0097] In some examples, the method for generating the dynamic decryption key is: concatenate the dynamic confusion factor σ (such as 0xE3) and the current system timestamp t (such as UNIX time 1620000000) and input them into the SHA3-512 hash function to generate a 256-bit key K_decrypt = SHA3-512(σ||t)[0:31], and intercept the first 32 bytes as the AES-256 key. The hierarchical decryption adopts a cascaded decryption mode: the first layer uses K_decrypt to decrypt the outer ciphertext of the data block to obtain the intermediate ciphertext C_mid; the second layer decrypts C_mid through the phase key K_phase = FFT(Q_noise)[amplitude sequence in the 3kHz - 5kHz frequency band] generated by the quantum noise spectrum characteristics. In the secondary integrity verification, calculate the Fourier transform phase spectrum Φ_data of the decrypted data, and perform a cosine similarity comparison with the phase perturbation baseline Φ_baseline collected in real time by the quantum noise source. If the similarity > 0.9, it is determined to be consistent. The deconfusion processing uses an exclusive OR operation, and uses the lower 8-bit bytes of σ to perform an exclusive OR operation on the data byte by byte for restoration.

[0098] Exemplarily, the user requests to obtain the encrypted file "Financial Report.docx". The system parses the security index S_index, extracts σ = 0xE3 and the implicit mapping {IDδ_Y → physical address 192.168.5.42:7049}. Based on the current timestamp 1620000000, generate K_decrypt = the first 32 bytes of SHA3-512("E31620000000") = 0x5a3d...c7f2. After locating the target data block, perform the first-layer AES decryption to obtain C_mid, and then use K_phase = [0.78π, 1.23π, 0.56π] for phase rotation decryption. Calculate the similarity between the decrypted data phase spectrum Φ_data and the real-time quantum noise Φ_baseline to be 0.93. After passing the verification, use σ = 0xE3 to perform XOR de-obfuscation on the data, and finally return the plaintext to the user terminal. At the same time, increasing the topological dimension parameter from 4 layers to 5 layers triggers a reconstruction.

[0099] Through the implementation of the above embodiments, the dynamic decryption key is deeply bound to the timestamp and the obfuscation factor, making the key for each access unique and time-sensitive, effectively defending against key reuse attacks; the hierarchical decryption mechanism combined with physical layer quantum feature verification ensures data integrity and source authenticity; the de-obfuscation process and the dynamic update of topological parameters form a closed-loop protection, preventing attackers from obtaining persistent access capabilities through a single crack, and realizing the full life-cycle security protection of data access and storage.

[0100] Furthermore, as an implementation of the foregoing method embodiments, the present application also provides a data index secure access device for implementing the foregoing method embodiments. This device embodiment corresponds to the foregoing method embodiments. For the convenience of reading, the details of the foregoing method embodiments will not be described one by one in this data index secure access device embodiment. However, it should be clear that the device in the embodiments of the present application can correspondingly implement all the contents of the foregoing method embodiments. Such as Figure 2 As shown in the figure, the data index security access device 20 includes: a topology structure generation unit 201, a verification path generation unit 202, a verification channel generation unit 203, a topology marking generation unit 204, a data index generation unit 205, and a data access unit 206. Among them, the topology structure generation unit 201 is configured to, in response to a data access instruction, generate a dynamic index topology structure that matches the current network latency jitter value based on the dynamic behavior fingerprint of the requesting terminal; the verification path generation unit 202 is configured to perform real-time reconstruction on the dynamic index topology structure based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint, and generate a collision-resistant verification path; the verification channel generation unit 203 is configured to inject a quantum noise signal into the collision-resistant verification path to form a cross-layer verification channel; the topology marking generation unit 204 is configured to perform an asymmetric hash chain verification on the target data index through the cross-layer verification channel, and generate a verification result including a blurred topology mark; the data index generation unit 205 is configured to perform dynamic remapping on the original index node according to the blurred topology mark, and output a collision-resistant and anti-parsing secure data index; the data access unit 206 is configured to perform data access based on the secure data index.

[0101] In some embodiments, the topology structure generation unit 201 is further configured to obtain the touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; perform non-linear fusion on the touch trajectory characteristics and network fluctuation characteristics through a convolutional chaos algorithm to generate a topology generation factor; construct a dynamic index topology structure in the complex domain space according to the topology generation factor, where the dimension parameter of the dynamic index topology structure is dynamically adjusted by the current network latency jitter value.

[0102] In some embodiments, the verification path generation unit 202 is further configured to decompose the spatio-temporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the space dimension; calculate a dynamic correlation matrix according to the entropy value of the sequence fluctuation characteristics and the variance of the trajectory distribution characteristics; perform real-time adjustment on the connection weights of the dynamic index topology structure based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; screen the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retain the paths that meet the preset anti-collision conditions as the collision-resistant verification paths, where the anti-collision conditions include that the path collision probability is less than a first preset threshold and the path jump count is greater than a second preset threshold.

[0103] In some embodiments, the verification channel generation unit 203 is further configured to obtain the path parameters of the anti-collision verification path, where the path parameters include path length, node distribution density, and hopping frequency; generate a quantum noise signal that matches the path parameters based on a quantum random number generator, where the spectral width of the quantum noise signal is positively correlated with the hopping frequency; perform convolution processing on the quantum noise signal and the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; inject the phase perturbation factor into the boundary between the physical layer and the logical layer of the anti-collision verification path to form a cross-layer verification channel, where the verification data packet of the cross-layer verification channel carries a noise fingerprint in the physical layer and embeds a dynamic hash tag in the logical layer.

[0104] In some embodiments, the topological tag generation unit 204 is further configured to construct an asymmetric hash chain structure based on the dynamic hash tag and the phase perturbation factor, where the public key of the asymmetric hash chain structure is generated from the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated from the expiration timestamp of the dynamic hash tag in the logical layer; perform cross-verification on the node attributes of the target data index to generate an initial verification result; perform iterative signature on the initial verification result using the asymmetric hash chain structure to obtain a multi-level verification signature; compare the multi-level verification signature with a preset anti-interference threshold, and if the threshold condition is met, extract the topological distribution characteristics of the multi-level verification signature; perform fuzzy encoding on the topological distribution characteristics based on the random mask factor in the quantum noise signal to generate a fuzzy topological tag, where the generation process of the fuzzy topological tag is dynamically associated with the dimension parameters of the dynamic index topological structure.

[0105] In some embodiments, the data index generation unit 205 is further configured to analyze the quantum noise spectrum characteristics and topological distribution parameters in the fuzzy topological tag to generate an anti-parsing mapping rule; perform dynamic offset on the storage location and access permission label of the original index node based on the anti-parsing mapping rule to generate an implicit mapping relationship; perform non-linear confusion on the implicit mapping relationship according to the dimension parameters of the dynamic index topological structure and the current system timestamp to generate a randomized index mapping table; inject a dynamic confusion factor into the randomized index mapping table to generate a secure data index resistant to parsing, where the generation frequency of the dynamic confusion factor is synchronously updated with the hopping frequency of the quantum noise signal.

[0106] In some embodiments, the data access unit 206 is further configured to, when detecting an abnormal parsing behavior for the secure data index, generate a virtual decoy index set according to the distribution characteristics of the fuzzy topological tag and cover the virtual decoy index set to the original index node; dynamically adjust the topological parameters of the virtual decoy index set based on the hopping frequency of the quantum noise signal to generate a confusion index network with an attack traceability function; when a virtual node in the confusion index network is activated for access, extract the behavior fingerprint characteristics of the attacker and trigger a data self-destruction instruction for the distributed storage node.

[0107] The present application also provides a computer-readable storage medium storing computer-executable instructions or a computer program, which, when executed by a processor, cause the processor to execute any step of the data index security access method provided by the present application.

[0108] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it may also be various devices including one or any combination of the above memories.

[0109] In some embodiments, the computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0110] In some embodiments, the computer-executable instructions may or may not correspond to a file in the file system, and may be stored as part of a file storing other programs or data. For example, they may be stored in one or more scripts in a HyperText Markup Language (HTML) document, stored in a single file dedicated to the program in question, or stored in multiple cooperating files (e.g., files storing one or more modules, subroutines, or code portions).

[0111] In some embodiments, the computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0112] As Figure 3 shown, the present application also provides an electronic device 30, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements any step of the above data index security access method.

[0113] The present application also provides a computer program product, which includes a computer program or computer-executable instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and the processor executes the computer program or computer-executable instructions, so that the electronic device executes any step of the data index security access method described above in the present application.

[0114] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.< / payload>

Claims

1. A method for secure access to data indexing, characterized in that Including: In response to a data access instruction, generate a dynamic index topology structure that matches the current network latency jitter value based on the dynamic behavior fingerprint of the requesting terminal; Based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint, perform real-time reconstruction on the dynamic index topology structure to generate an anti-collision verification path; Inject a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel; Perform asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result containing a blurred topology mark; Perform dynamic remapping on the original index node according to the blurred topology mark, and output a secure data index resistant to parsing; Perform data access based on the secure data index.

2. The data index security access method according to claim 1, wherein The generating of the dynamic index topology structure that matches the current network latency jitter value based on the dynamic behavior fingerprint of the requesting terminal includes: Obtain the touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; Non-linearly fuse the touch trajectory characteristics and network fluctuation characteristics through a convolutional chaos algorithm to generate a topology generation factor; Construct the dynamic index topology structure in the complex domain space according to the topology generation factor, where the dimension parameter of the dynamic index topology structure is dynamically adjusted by the current network latency jitter value.

3. The data index security access method according to claim 2, wherein The performing of real-time reconstruction on the dynamic index topology structure based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint to generate an anti-collision verification path includes: Decompose the spatio-temporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the space dimension; Calculate a dynamic correlation matrix according to the entropy value of the sequence fluctuation characteristics and the variance of the trajectory distribution characteristics; Based on the dynamic correlation matrix, perform real-time adjustment on the connection weights of the dynamic index topology structure to generate a reconstructed topology network with a random perturbation factor; Screen the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retain the paths that meet the preset anti-conflict conditions as the anti-collision verification path, where the anti-conflict conditions include that the path conflict probability is less than a first preset threshold and the path jump count is greater than a second preset threshold.

4. The data index security access method according to claim 1, characterized in that, The injecting of a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel includes: Obtain the path parameters of the anti-collision verification path, where the path parameters include path length, node distribution density, and jump frequency; Generate a quantum noise signal that matches the path parameters based on a quantum random number generator, where the spectral width of the quantum noise signal is positively correlated with the jump frequency; Perform convolution processing on the quantum noise signal and the topology parameters of the anti-collision verification path through a chaos modulation algorithm to generate a phase perturbation factor; Inject the phase perturbation factor into the boundary between the physical layer and the logical layer of the anti-collision verification path to form a cross-layer verification channel, where the verification data packet of the cross-layer verification channel carries a noise fingerprint in the physical layer and embeds a dynamic hash mark in the logical layer.

5. The data index security access method according to claim 4, wherein, The performing of asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result containing a blurred topology mark includes: Based on the dynamic hash tag and the phase perturbation factor, construct an asymmetric hash chain structure, wherein the public key of the asymmetric hash chain structure is generated from the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated from the expiration timestamp of the dynamic hash tag in the logical layer; Perform cross-verification on the node attributes of the target data index to generate an initial verification result; Use the asymmetric hash chain structure to perform iterative signature on the initial verification result to obtain a multi-level verification signature; Compare the multi-level verification signature with a preset anti-interference threshold. If the threshold condition is met, extract the topological distribution characteristics of the multi-level verification signature; Perform fuzzy coding on the topological distribution characteristics based on the random mask factor in the quantum noise signal to generate the fuzzy topological tag, wherein the generation process of the fuzzy topological tag is dynamically associated with the dimension parameters of the dynamic index topological structure.

6. The data index security access method according to claim 1, characterized in that The dynamic remapping of the original index node according to the fuzzy topological tag to output a secure data index resistant to parsing includes: Analyze the quantum noise spectrum characteristics and topological distribution parameters in the fuzzy topological tag to generate an anti-parsing mapping rule; Dynamically offset the storage location and access permission label of the original index node based on the anti-parsing mapping rule to generate an implicit mapping relationship; Perform non-linear confusion on the implicit mapping relationship according to the dimension parameters of the dynamic index topological structure and the current system timestamp to generate a randomized index mapping table; Inject a dynamic confusion factor into the randomized index mapping table to generate the secure data index resistant to parsing, wherein the generation frequency of the dynamic confusion factor is synchronously updated with the jump frequency of the quantum noise signal.

7. The data index security access method according to claim 1, characterized in that The secure access method for data index further includes: When detecting an abnormal parsing behavior for the secure data index, generate a virtual decoy index set according to the distribution characteristics of the fuzzy topological tag, and cover the virtual decoy index set to the original index node; Dynamically adjust the topological parameters of the virtual decoy index set based on the jump frequency of the quantum noise signal to generate a confusion index network with an attack traceability function; When a virtual node in the confusion index network is activated for access, extract the behavior fingerprint characteristics of the attacker and trigger the data self-destruction instruction of the distributed storage node.

8. A data index security access device, characterized in that, Include: A topological structure generation unit for generating a dynamic index topological structure matching the current network delay jitter value based on the dynamic behavior fingerprint of the request terminal in response to a data access instruction; A verification path generation unit for performing real-time reconstruction on the dynamic index topological structure based on the spatio-temporal distribution characteristics of the dynamic behavior fingerprint to generate a collision-resistant verification path; A verification channel generation unit for injecting a quantum noise signal into the collision-resistant verification path to form a cross-layer verification channel; A topological tag generation unit for performing asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result including a fuzzy topological tag; A data index generation unit, configured to perform dynamic remapping on original index nodes according to the fuzzified topology tags, and output a secure data index resistant to parsing. A data access unit, configured to perform data access based on the secure data index.

9. An electronic device, comprising: A memory and a processor, characterized in that when the processor executes a computer program stored in the memory, it implements the steps of the data index secure access method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the data index secure access method according to any one of claims 1-7.

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