Data crushing method based on four-dimensional manifold chaotic power system

Abstract

The invention relates to the technical field of data security, and discloses a data crushing method based on a four-dimensional manifold chaotic power system, and the method comprises the steps: generating a time-space coupled chaotic entropy source through system initialization, dynamically distributing entropy decomposition channels for input data according to type characteristics, and achieving the irreversible crushing and entropy control of the data; and then fragment-level distributed verification is cooperatively completed by means of edge nodes, an on-chain certificate with auditing performance is generated, real-time closed-loop updating is performed on chaotic parameters in combination with a verification result, and a self-adaptive feedback control mechanism is formed. According to the method, a Merkle-Patricia tree structure is improved to realize non-tampering and chained backtracking of data, a security signature based on a lattice structure is adopted to ensure the long-term effectiveness of verification information, and the stability of an entropy source is optimized through a chaotic disturbance feedback mechanism, so that the security, reliability and adaptability of a system under the threat of a high-complexity calculation model are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, in particular to a data shredding method based on a four-dimensional manifold chaotic dynamic system. Background Art

[0002] As information systems continue to grow in complexity, the demand for high-strength encryption mechanisms and high-entropy randomness sources is growing in data processing, transmission, and verification. In particular, in typical scenarios such as blockchain, edge computing, and privacy-preserving computing, system security often relies on highly random, unpredictable entropy generation mechanisms and the high reliability of multi-node collaborative verification. However, existing pseudo-random number generators and distributed verification systems generally suffer from high predictability, unstable entropy values, and a lack of feedback mechanisms, making them difficult to meet the data integrity and attack resistance requirements of high-security scenarios.

[0003] Traditional encryption schemes rely heavily on classical algorithms and low-level dynamic perturbation mechanisms, making them inadequate for increasingly complex attack models (including speculation attacks, replay attacks, and perturbation injection). Furthermore, in resource-constrained edge nodes, dynamic adjustment and load-adaptive control of entropy source generators and encryption modules are difficult to implement, leading to frequent problems such as entropy degradation, poor data consistency, and difficulty maintaining system stability.

[0004] Therefore, the main defects in the existing technology are: the lack of a mechanism that can realize adaptive entropy source regulation and feedback control based on the operating status, especially in applications that support distributed architecture and high dynamic security requirements, the lack of a unified high-entropy data processing and system closed-loop update mechanism.

[0005] In view of the above defects, the present invention proposes a data shredding method based on a four-dimensional manifold chaotic dynamic system. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a data shredding method based on a four-dimensional manifold chaotic dynamic system to solve the core security problems of the existing blockchain system under the threat of high-complexity computing models, such as the fragility of encryption algorithms, easy tampering of verification information, and insufficient randomness of entropy sources.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data crushing method based on a four-dimensional manifold chaotic dynamic system, comprising the following steps:

[0008] System initialization: configure and activate the four-dimensional manifold chaotic dynamic system to generate a spatiotemporal coupled chaotic entropy source;

[0009] Data classification processing: Identify the type characteristics of input data and dynamically select the corresponding entropy decomposition channel;

[0010] Entropy decomposition processing: irreversibly compress the data according to the selected channel and output a set of fragments that meet the entropy value standard;

[0011] Distributed verification: Collaborative verification of shard sets is performed through edge computing nodes to generate auditable verification credentials;

[0012] Closed-loop update: The verification certificate is written into the blockchain, and the system chaos parameters are updated according to the stored results to form a feedback control closed loop.

[0013] Preferably, in the system initialization step, the four-dimensional manifold chaotic dynamic system is defined by the following fractional-order differential equations:

[0014]

[0015] Where α is the fractional differential order, σ is the spatial coupling coefficient, ρ is the nonlinear feedback coefficient, β is the dissipation coefficient, γ is the time tensor coupling strength, η is the time flow feedback coefficient, κ is the spatiotemporal synchronization coefficient, λ is the time flow attenuation coefficient, T is the local time flow variable, and W t is the time-dependent feature tensor.

[0016] Preferably, the data classification processing step includes:

[0017] Multimodal feature extraction:

[0018] Text data: Calculate the average path length L of the dependency syntax tree avg ≥2.5;

[0019] Image data: The spectral radius R of the extracted convolution feature map is ≥ 0.7;

[0020] Video data: Calculate the fractal dimension D of the optical flow field f ≥1.8;

[0021] Routing decision execution:

[0022]

[0023] The priority of logical operators is: first perform data type matching, then perform feature threshold judgment; L avg is the average path length of the dependency syntax tree, R is the spectral radius of the convolutional feature map, and D is the input data stream.

[0024] Preferably, the entropy decomposition processing step includes, when selecting the text decomposition channel:

[0025] Constructing grammatical entanglement state;

[0026] applying a decoherence gate operation;

[0027] Irreversible fragments are generated through projection measurement, satisfying the entropy value ≤ 0.01 bit / word.

[0028] Preferably, the entropy decomposition processing step includes, when selecting the image decomposition channel:

[0029] Implementing metasurface optical convolution;

[0030] Generate chaotic speckle patterns, satisfying the structural similarity index SSIM ≤ 0.05;

[0031] Verify mutual information entropy.

[0032] Preferably, the entropy decomposition processing step includes, when selecting the video decomposition channel:

[0033] Constructing the space-time crystal Hamiltonian;

[0034] Applying a critical temperature field triggers the phase transition;

[0035] Output the annihilated video cube, satisfying PSNR ≤ 10dB.

[0036] Preferably, in the distributed verification step:

[0037] Key material required for collaborative verification: Based on the chaotic entropy source generated in the system initialization step, verification keys are generated in a time series;

[0038] Generate auditable verification credential algorithm: The final verification credential is generated by collaboratively performing tensor operations on edge nodes and the cloud.

[0039] Preferably, in the closed-loop updating step:

[0040] Blockchain evidence storage: The verification credentials generated by the distributed verification step are written to the blockchain and stored using an improved Merkle-Patricia tree structure;

[0041] Parameter closed-loop update: Adjust the chaotic system parameters based on the stored evidence results.

[0042] Preferably, measures to resist high-complexity attacks are also included:

[0043] Integrate a high entropy random number generator in the entropy decomposition process, with a generation rate of ≥100Mbps;

[0044] Embed lattice-based digital signatures in the verification credentials of the distributed verification step.

[0045] The present invention provides a data shredding method based on a four-dimensional manifold chaotic dynamic system. It has the following beneficial effects:

[0046] 1. This invention achieves highly reliable recording of multi-stage verification information and chain-based backtracking of historical states by adopting an "unalterable blockchain evidence storage mechanism based on an improved Merkle-Patricia tree structure." Compared with the trust bottleneck caused by existing solutions that rely on local cache or centralized database storage, this invention avoids the risks of forgery and overwriting in the verification chain from the underlying architecture, providing structural support for traceability audit scenarios in complex distributed environments, and effectively filling the gaps in existing technologies in terms of tamper-proof verification credentials and global consistency.

[0047] 2. By introducing a lattice-based tamper-resistant signature embedding mechanism and integrating it into a distributed authentication credential structure, this invention achieves dual protections for structural anti-counterfeiting and resistance to complex attacks during the transmission and on-chain recording of authentication information. Compared to traditional RSA or elliptic curve signature schemes, this invention offers greater security margins and scalability, providing a technical foundation for future high-security communications.

[0048] 3. This invention combines a ≥100Mbps high-entropy random number generator with chaotic perturbation control logic based on feedback formulas to construct a dynamically updated chaotic entropy source feedback system. This system maintains perturbation unpredictability and stable output performance when processing high-frequency data, addressing the issues of slow response to entropy source fluctuations and single perturbation parameters in existing methods. This solution, through real-time closed-loop parameter self-adjustment, mitigates the risk of system instability or entropy degradation under high load or abnormal conditions, achieving a dual balance between safety and performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] Please see the attached Figure 1 The embodiment of the present invention provides a data shredding method based on a four-dimensional manifold chaotic dynamic system, comprising the following steps:

[0052] S1. System initialization: configure and activate the four-dimensional manifold chaotic dynamic system to generate a spatiotemporal coupled chaotic entropy source;

[0053] In this implementation, system initialization aims to construct a four-dimensional manifold chaotic dynamical system with a high-dimensional coupling structure and fractional-order evolutionary characteristics. This generates a chaotic entropy source with high entropy, strong nonlinearity, and time-space linkage, providing initial dynamic support for the entire data shredding process. Through precise modeling, parameter control, and tensor construction, this system achieves both physical feasibility and theoretical complexity in engineering implementation.

[0054] Chaotic system construction model:

[0055] In general, in order to realize the complex evolutionary behavior and adjustable chaotic output of the system, the following fractional differential form is used to construct a four-dimensional dynamic model:

[0056]

[0057] In the above model, x(t), y(t), z(t) represent the three-dimensional spatial coupling state variables, T(t) represents the system local time flow variable; α∈(0,1) represents the fractional order of the system, which is used to describe the historical dependence and memory decay properties; σ is the spatial coupling coefficient, ρ is the nonlinear feedback coefficient, β is the dissipation coefficient, γ is the time tensor coupling strength, η is the time flow feedback coefficient, κ is the space-time synchronization coefficient, λ is the time flow decay coefficient, T is the local time flow variable, W t is the time-dependent feature tensor.

[0058] In some implementations, the fractional-order model is discretized using the Caputo differential definition, which is more suitable for solving practical problems in digital logic and hardware systems. To ensure that the system has sufficient chaotic characteristics, the parameter ranges of each variable are set as follows:

[0059] α∈[0.8,0.9]: non-integer order dynamic response of the control system evolution;

[0060] σ∈[8,12]: adjusts the interaction strength between x and y, affecting the energy transfer path;

[0061] ρ∈[26,30]: as a nonlinear feedback factor, it regulates whether the system enters a chaotic state;

[0062] β∈[2.5, 3.5]: dissipation factor, which mainly controls the energy decay rate of the system;

[0063] γ∈[0.1,0.5]: controls the degree of disturbance of the time-dependent tensor on the spatial variable;

[0064] η∈[1.2,1.8]: describes the weight of the reaction of the local time flow to the spatial state;

[0065] κ∈[0.8,1.0]: coupling modulation factor, which determines the spatiotemporal dynamical connection;

[0066] λ∈[2.3,2.7]: adjusts the stability and self-feedback rate of T(t).

[0067] In a typical implementation, the time disturbance term W t As a coupling term in the time dimension, its construction is as follows:

[0068]

[0069] in:

[0070] ω k ∈[0.1,0.5]: exponential decay weight coefficient, controlling the intensity of the disturbance;

[0071] τ k ∈[1,5]: time scale adjustment parameter, which controls the decay rate;

[0072] ξ k : a set of three orthogonal basis tensors that satisfy the orthogonality condition <ξ i ,ξ j >=δ ij , and the Frobenius norm ||ξ k || F =1;

[0073] This tensor structure introduces non-stationary perturbations on the time axis, effectively increasing the unpredictability of the system.

[0074] Alternatively, the tensor generation process can be implemented via a high-dimensional orthogonal transformation matrix, such as the Gram-Schmidt orthogonalization algorithm.

[0075] In order to make the system in a strongly chaotic state at the initial state, the maximum Lyapunov exponent optimization method is generally used:

[0076] Initialize the parameter set Θ = {σ,ρ,β,γ,η,κ,λ};

[0077] Solving the Maximum Lyapunov Exponent λ Based on Orbital Perturbation max (Θ);

[0078] Apply the gradient ascent method for iterative updates:

[0079]

[0080] Among them, ∈ is the learning rate, which can be dynamically adjusted according to the system convergence. The system state obtained after optimization must satisfy λ max ≥0.6, ensuring that the output entropy source meets the requirements of high nonlinearity and high sensitivity.

[0081] In some application scenarios, chaotic systems can be implemented in hardware. For example:

[0082] The fractional order derivative module is based on the discretization design of Caputo formula and supports dynamic order control

[0083] The tensor co-processing unit implements the construction and real-time operation of WtWt, using a three-dimensional tensor multiplication logic structure

[0084] The parameter input module supports SPI / AXI communication interface and supports synchronous interaction with the main control CPU or MCU

[0085] During initialization, the module automatically loads the initial chaotic parameters via the DMA channel and performs the first round of chaotic sequence generation. The generated sequence is written to the local cache for subsequent use, ensuring that the system can seamlessly enter the data processing phase.

[0086] As the core initial module of the entire system, the chaotic dynamic system has the following connection relationship with other steps:

[0087] The calculation of feature quantitative indicators in step data classification processing needs to rely on the distribution function generated by the chaotic entropy source for dynamic threshold adjustment;

[0088] The key generation operation in step distributed verification uses the following formula:

[0089] K i =SHA3-512(x(t i )⊕y(t i )⊕z(t i ));

[0090] Among them, t i It is the system time slice with an interval of 50ms;

[0091] The parameter feedback of the step closed-loop update depends on the current state of the chaotic system to achieve system-level adaptive closed-loop optimization update.

[0092] S2, data classification processing: identify the type characteristics of input data and dynamically select the corresponding entropy decomposition channel;

[0093] This implementation step is the data processing phase after system initialization. It aims to achieve refined classification and recognition based on the modal characteristics of the input data and select an appropriate entropy decomposition channel based on the identified feature structure. This step directly relies on the spatiotemporal chaotic entropy source constructed in the system initialization step (S1). Its output sequence not only participates in the dynamic adjustment of feature thresholds but also initializes the perturbation template required for feature extraction, thus forming a continuous data recognition and processing pathway.

[0094] Generally speaking, after receiving the input data stream (denoted as D), the data classification module first provides the dynamic disturbance parameter set from the chaotic entropy source module. It is used to enhance the feature responsiveness of multimodal data and then enters the modal identification and channel selection submodule.

[0095] In this embodiment, multimodal feature extraction includes the following key processing steps:

[0096] In one possible implementation, the system extracts key structural features through a modality matching strategy based on data type, where:

[0097] For text data, we first construct a dependency syntactic tree structure and calculate the average length of all syntactic paths, which is defined as follows:

[0098]

[0099] in:

[0100] N represents the total number of syntactic edges;

[0101] l i represents the path length corresponding to the i-th edge;

[0102] The judgment condition is: if It is initially judged to be text modal input.

[0103] For image data, a pre-trained convolutional neural network is used to extract the intermediate layer feature map and calculate its spectral radius R, which is defined as follows:

[0104] R=max{|λ i ||λ i ∈Spec(F)};

[0105] Among them, F represents the convolution feature map mapping matrix; λ i is the set of eigenvalues ​​of the feature graph; Spec(F) represents the spectrum of the matrix F; and the structural similarity index SSIM(D) is combined for joint judgment. If the condition is met: R ≥ 0.7 ∧ SSIM(D) > 0.7, the input data is classified as image modality.

[0106] For video data, in general implementation, the system uses multiple frames of images to calculate the optical flow field, and then estimates its fractal dimension D based on the Hausdorff method. f , the expression is as follows:

[0107]

[0108] Among them, N(∈) represents the number of balls required to cover the minimum optical flow vector field; ∈ is the coverage scale; the judgment threshold is D f ≥1.8.

[0109] In this step, the system selects the subsequent entropy decomposition channel according to the above modal feature extraction results through the following logical rules:

[0110]

[0111] In this rule, the priority of logical judgment follows the principle that data type identification takes precedence over feature judgment, that is, the system first classifies the input mode, and then calculates the threshold function based on the classified mode to determine whether it meets the channel conditions.

[0112] Specifically, the system introduces the chaotic entropy source variable constructed from the system initialization phase during the feature extraction process to modulate the input features:

[0113] In some embodiments, by perturbation transformation:

[0114] D′(t)=D(t)⊕(γW t +ηT(t));

[0115] Where D(t) is the original input data; ⊕ represents the XOR perturbation at the tensor level; γ is the temporal tensor modulation intensity, which controls the perturbation amplitude; W t is the chaotic characteristic tensor, defined as: T(t) is the time flow variable, which comes from the fourth term of the system differential equations; η is the feedback coefficient, which is used to adjust the feedback intensity of the time flow on the disturbance effect;

[0116] This perturbation process significantly improves the discrimination of feature judgment and enhances the adaptability to atypical data.

[0117] As an option, at the hardware implementation level, the data classification module is integrated into the high-frequency task sequence of the tensor coprocessor:

[0118] Operations such as convolution spectrum extraction, dependency structure analysis, and optical flow estimation are all initialized through the distributed perturbations provided by the chaotic entropy source;

[0119] The tensor buffer inside the coprocessor is preloaded with the orthogonal basis tensor ξ k , to achieve W t Real-time construction;

[0120] The system receives the current input data stream from the main control MCU through the SPI bus and performs modulation processing;

[0121] In addition, in some scenarios, a DMA channel linkage mechanism exists between the modal determination module and the initialization module to ensure that the current state parameter set Θ = {σ,ρ,β,γ,η,κ,λ} can still be quickly synchronized when the system is powered on again or the channel is switched.

[0122] The coupling mechanism between this step and the system initialization step (S1) is reflected in the following aspects:

[0123] The thresholds of 2.5, 0.7, and 1.8 required for modal recognition can be dynamically fine-tuned based on the distribution density of the chaotic sequence output;

[0124] The selected perturbation tensor W t It comes from the exponential decay tensor structure defined by the initialization model;

[0125] The real-time values ​​of system state variables x(t), y(t), z(t), and T(t) are used as characteristic disturbance reference quantities to participate in characteristic judgment;

[0126] The parameter control mechanism works in conjunction with subsequent channel configuration to ensure the stability of the overall logic closed loop.

[0127] S3, entropy decomposition processing: irreversibly compress the data according to the selected channel and output a set of fragments that meet the entropy value standard;

[0128] In this embodiment, the entropy decomposition processing step is designed to perform irreversible information compression and structural perturbation on the input data according to the selected channel, and generate a set of data fragments that meet the entropy value standard. The core of this step is to achieve the coexistence of structural destructiveness and feature traceability of the data through perturbation enhancement and feature coupling mechanisms, ensuring that the data meets the entropy control index during the compression process while retaining sufficient feature information for subsequent processing. The entropy decomposition process selects and processes specific channels based on the selected data type (such as text, image or video).

[0129] Generally speaking, the data entropy decomposition processing steps include different processing methods for the three data modalities of text, image and video, and setting different decomposition mechanisms according to the characteristics of each data to ensure that its entropy value meets the requirements.

[0130] In one possible implementation, for text data, entropy decomposition processing includes the following steps:

[0131] Construct a dependency syntax tree of the text;

[0132] Introduce a perturbation mapping matrix to perturb the syntactic structure;

[0133] Through semantic mapping compression operation, information is compressed into a sparse embedding space;

[0134] Finally, a projection mapping mechanism is applied to generate irreversible text fragments.

[0135] The output text fragments must meet the following entropy standards:

[0136] H≤0.01bit / word;

[0137] Here, H represents the entropy value of the text fragment, in bits / words. The entropy value must be less than or equal to 0.01 to ensure high compressibility and irreversibility of the data.

[0138] This threshold ensures that the fragments are difficult to reconstruct into the original sentence structure while retaining a certain amount of information.

[0139] In another implementation, the system uses chaotic perturbation convolution (CD-CNN) combined with image texture perturbation strategy to decompose image data. Specifically, it includes:

[0140] Extract high-frequency structural features of the image and perform chaotic perturbation mapping;

[0141] Constructing speckle perturbation maps to enhance the nonlinear distribution of data;

[0142] The structural similarity index (SSIM) and mutual information entropy were evaluated.

[0143] The processing results should meet the following two constraints:

[0144] SSIM(D)≤0.05;

[0145] Where D is the image data and SSIM is an important indicator for measuring image quality. By controlling the SSIM value below 0.05, the chaotic characteristics of the image fragments are guaranteed.

[0146] The mutual information entropy decreases, satisfying:

[0147] I(X;Y)=H(X)+H(Y)-H(X,Y)I(X;Y)=H(X)+H(Y)-H(X,Y)

[0148] In addition, during the processing, it is also necessary to verify the mutual information entropy of the image to further ensure that its entropy value meets the requirements. The verification formula for mutual information entropy is as follows:

[0149] I(X;Y)=H(X)+H(Y)-H(X,Y);

[0150] Where I(X;Y) is the mutual information between the original image data and the entropy decomposition result, H(X) and H(Y) are the individual information entropies of the image data and the corresponding entropy decomposition fragments, respectively, and H(X,Y) is the joint entropy of the image data and the N decomposed fragments. This step evaluates whether the decomposed fragments still retain information redundancy that can be used to reconstruct the original image by measuring the mutual information. This ensures that the image data, after entropy decomposition, meets the requirements of irreversible compression while maximally retaining key feature information, facilitating subsequent structural verification and security mapping.

[0151] In another possible implementation, for video data, the system constructs a video perturbation channel based on the spatiotemporal perturbation Hamiltonian mapping model:

[0152] First, extract the optical flow features and calculate its fractal dimension;

[0153] Construct a perturbation cube representation structure;

[0154] Finally, by applying a temperature-controlled disturbance field, local phase change is induced to achieve irreversible compression.

[0155] The generated video fragments must meet the following peak signal-to-noise ratio constraints:

[0156] PSNR≤10dB;

[0157] PSNR (Peak Signal-to-Noise Ratio) is a measure of the quality of compressed video data. A PSNR value of 10dB or less indicates that the quality of the video reconstruction has been significantly degraded after entropy decomposition and structural perturbation compression, and the data content exhibits a high degree of irreversibility and structural fragmentation.

[0158] This limit ensures that the compression result cannot be used to visually reconstruct the original video content.

[0159] In this embodiment, the channel selection for the entropy decomposition step depends on the data type identification result. Specifically, the system determines the modality of the input data and selects the corresponding entropy decomposition processing channel. If the input data is text, the system selects the text decomposition channel; if the input data is an image, the system selects the image decomposition channel; if the input data is a video, the system selects the video decomposition channel.

[0160] Connection between channel selection and entropy decomposition processing:

[0161] When selecting the entropy decomposition channel, the system first relies on the aforementioned data classification processing steps (such as modal identification) to determine the decomposition channel based on the type of input data. At this time, the implementation method of the entropy decomposition process is closely related to the selected channel:

[0162] For text data, the system will first construct a dependency syntax tree, calculate the average path length, and then enter the text decomposition channel if the conditions are met;

[0163] For image data, the system will extract the convolution feature map, calculate the spectral radius and structural similarity index, and enter the image decomposition channel if it meets the conditions;

[0164] For video data, the system will calculate the fractal dimension of the optical flow field and enter the video decomposition channel if it meets the conditions.

[0165] This close connection between channel selection and decomposition process ensures that each data type can be fully processed in the appropriate entropy decomposition channel to generate an irreversible fragment set that meets the entropy value criteria.

[0166] S4, Distributed Verification: Collaborative verification of shard sets through edge computing nodes to generate auditable verification credentials;

[0167] After completing the entropy decomposition processing step (S3), the system enters the distributed verification step (S4), which ensures the security, integrity, and auditability of the set of perturbed fragments generated by each entropy decomposition channel of the system. This step aims to leverage the synergy between edge computing nodes and the central cloud platform to verify the structural consistency and content traceability of the irreversible fragment sets output by each channel and generate verification credentials with audit capabilities. This step is highly related to the chaotic entropy source construction mechanism in the system initialization step, as well as the modal identification parameters and entropy decomposition channel selection mechanism provided by the data classification processing module.

[0168] Generally speaking, after completing the entropy decomposition processing steps, the system will automatically call the distributed verification mechanism, and the edge computing node will perform local verification according to the established time sequence and key update strategy, and upload the intermediate verification results to the cloud for final convergence calculation to generate standardized verification credentials.

[0169] In this embodiment, the key material for collaborative verification comes from the chaotic entropy source sequence constructed in the system initialization step.

[0170] In one possible implementation, the system generates a key stream for verification based on the following time evolution function:

[0171]

[0172] Among them, K t The verification key generated at time t; is the hash convergence function, used to enhance the nonlinear expansion of the key; α i The chaos weight factor defined for system initialization; ψ i (t) correspond to the chaotic source variables x(t), y(t), z(t), T(t); ∈ t is the perturbation residual term, which comes from the time domain tensor perturbation W t The residual distribution of

[0173] In general, the system uses K t Real-time updates are used to maintain unpredictability in the verification process while avoiding the risk of data reconstruction caused by key reuse.

[0174] In this embodiment, the verification logic in which the edge nodes participate can be abstracted as a tensor-level collaborative hash mapping process.

[0175] Specifically, the system records each fragment set as a tensor form S i , and proceed as follows:

[0176] First, each fragment tensor is normalized:

[0177]

[0178] Among them, μ i is the mean of the i-th fragment tensor; σ i is the corresponding standard deviation;

[0179] Then hash encoding is performed through the edge node collaborative mapping function:

[0180]

[0181] Among them, V i Local verification vector output by the edge node; The mapping function is usually a nonlinear compression tensor mapping network; ⊕ represents a bit-level mixing perturbation operation;

[0182] In some embodiments, the mapping function adopts a self-supervised tensor network structure and has the ability to adaptively adjust the projection dimension, thereby improving the robustness of edge nodes in processing fragment differences.

[0183] In this embodiment, the cloud-based verification convergence process is implemented based on a tensor merging mechanism and symmetric consistency measurement.

[0184] In a specific implementation, all edge nodes upload local verification vectors V i Is incorporated into the global verification tensor G for fusion processing:

[0185] The system further constructs the symmetric tensor kernel function Used to compare the consistency of the global tensor and each local result:

[0186]

[0187] Among them, Λ i is the consistency score; <·,·> is the tensor inner product; ||·|| is the tensor two norm; the system sets the threshold Λ i ≥0.85 is the passing standard, and below the threshold triggers the re-verification process and records the audit log.

[0188] In this embodiment, the algorithm for generating the final verification credential includes the following processing flow:

[0189] As an option, the system is based on the timestamp T s , key index κ and verification tensor summary value δ generate auditable verification credentials:

[0190] Π=Enc SHA-512 (T s ||κ||δ||θ);

[0191] Where Π is the final generated verification credential; || is the splicing operation; θ is the summary of the current system status, including system load, link delay, temperature disturbance, etc. It is the hash summary value of the global verification tensor; the verification certificate has traceability and tamper-proof capabilities. All verification certificates will be recorded in the system's distributed security chain (DSC). This mechanism draws on the audit chain structure in "Quantum Key Distribution (QKD)", but this system does not rely on real quantum physics mechanisms. Instead, it achieves verification security by perturbing the hash key stream and chaotic weight function, ensuring that each verification is traceable and tamper-proof.

[0192] The distributed verification step plays a key role in security and verification throughout the entire system process, and is associated with the aforementioned modules as follows:

[0193] The verification key used is derived from the chaotic entropy source generated during the initialization phase, ensuring the unity of temporal continuity and randomness.

[0194] The verification process is performed on the fragment collection {S i The processing of} is based on the entropy decomposition processing output structure to ensure that the verification input has a standardized tensor format;

[0195] The perturbation function used in the collaborative verification process and the tensor perturbation model W introduced in the entropy decomposition processing stage t Maintain consistency;

[0196] The final verification credential π will be embedded in the access authorization link of the subsequent data reconstruction step, playing the role of chain authentication.

[0197] S5. Closed-loop update: Write the verification certificate into the blockchain, and update the system chaos parameters based on the stored evidence to form a feedback control closed loop.

[0198] After completing the distributed verification step (S4), the system enters the closed-loop update step (S5). This step aims to ensure the long-term traceability of the verification results and to provide feedback control over the system based on the stored evidence, further optimizing the chaotic entropy source and other key parameters. By writing the verification credentials to the blockchain, the system can establish an immutable chain of records and update the system's chaotic parameters based on the verification credentials, forming an adaptive closed-loop feedback mechanism to ensure that the system is always under effective control.

[0199] In one possible implementation, the system will write the generated verification credentials to the blockchain through the following steps:

[0200] Generate blockchain evidence: After the verification credential is generated, the system first calculates its hash value to ensure data consistency and tamper resistance. This hash value is then written to the blockchain as part of the transaction, along with relevant information such as the verification timestamp, verification key index, and system status summary. This step not only ensures the secure storage of the verification credential but also provides a reliable basis for subsequent auditing and traceability.

[0201] Π block =SHA-256(Π||T s ||κ||θ);

[0202] Among them, Π block represents the hash of the certificate on the blockchain, Π is the verification certificate, T s is the timestamp, κ is the verification key index, and θ is the summary of the current state of the system. The concatenation of the stored information is followed by a hash calculation to ensure that the information cannot be tampered with.

[0203] Blockchain Storage and Verification: Evidence is disseminated across the blockchain network and ultimately verified and confirmed by each node. Once information is written to the blockchain, any third party can access the evidence and verify its validity based on the information recorded on the blockchain. This process provides the system with robust audit capabilities, ensuring traceability and tamper-proofing at every stage of data generation and verification.

[0204] After blockchain evidence is stored, the system uses this information to adjust key system parameters, such as the chaotic entropy source. Specifically, the system dynamically updates chaotic parameters using a feedback control algorithm based on the blockchain's verification credentials and the current system's operating status, ensuring system stability and security.

[0205] In general, the system will adjust the chaotic entropy source parameters based on the following feedback formula:

[0206] X t+1 =X t +γ·ΔX;

[0207] Among them, X t represents the chaotic state parameter of the system at the current moment; X t+1 represents the new chaotic parameter after feedback adjustment; γ is the feedback adjustment coefficient; ΔX is the adjustment amount calculated based on the verification results, which is usually equal to the verification consistency score Λ i is associated with the system state summary θ.

[0208] By continuously updating the chaotic parameters, the system can adaptively optimize the entropy source disturbance during the entropy decomposition process, thereby enhancing the security and anti-interference capability of the data processing process.

[0209] In one possible implementation, the feedback mechanism involves the following steps:

[0210] Verification consistency score feedback: When the evidence certificate in the blockchain is verified and the consistency score is Λ i If the consistency score is higher than a preset threshold, the system will use this information to fine-tune the disturbance of the chaos source. If the consistency score is lower than the threshold, the system needs to trigger a backtracking check or recalibration process to ensure system stability.

[0211] Chaos perturbation and optimization: The system adjusts the perturbation amplitude of the entropy source based on feedback from each verification process. Specifically, if the system detects an abnormal verification result, it may increase the perturbation of the entropy source, increasing the randomness of the entropy decomposition process. Conversely, it may reduce the perturbation and reduce unnecessary computing resource consumption.

[0212] Through this feedback mechanism, the system can continuously optimize performance during the entropy decomposition processing and verification process, ensuring that each data processing and verification process meets the design standards and continuously adapts to external changes.

[0213] Example of closed-loop feedback control:

[0214] For example, if the system detects an anomaly during a verification phase (such as a verification failure or a low consistency score), it automatically adjusts the perturbation parameter X of the chaotic entropy source, for example by increasing the perturbation range to enhance the randomness of the data. This makes the fragments generated in the subsequent entropy decomposition process more unpredictable, thereby enhancing the system's resistance to external attacks and ensuring data security and integrity.

[0215] It also includes measures to resist highly complex attacks:

[0216] Integrate a high entropy random number generator in the entropy decomposition process, with a generation rate of ≥100Mbps;

[0217] Embed lattice-based digital signatures in the verification credentials of the distributed verification step.

[0218] During the entropy decomposition process (S3) and distributed verification (S4), the system's anti-complexity computing attack measures are designed to ensure a high degree of security and unpredictability in the data processing process. This protection is primarily achieved by integrating a high-entropy random number generator and lattice-based secure digital signature technology. The high-entropy random number generator generates high-entropy random numbers at high speed, providing a stronger entropy source for the system. The lattice-based secure digital signature technology is used to verify the signature embedded in the credential, enhancing the data's tamper resistance.

[0219] In system design, the need for resistance to high-complexity computational attacks permeates every step of the entropy decomposition process, distributed verification, and closed-loop updates. A high-entropy random number generator plays a crucial role in the entropy decomposition process. It not only provides the system with a high-quality random number source but also provides sufficient unpredictability for subsequent key generation, verification, and other critical steps. Lattice-based digital signatures, embedded in the verification credentials generated by distributed verification, enhance the system's resistance to high-complexity computational attacks.

[0220] In this embodiment, the system integrates a high-entropy random number generator (QRNG). Its operating principle is to generate random numbers with a sufficiently strong entropy source based on the unpredictability of physical phenomena such as noise, meeting the high randomness requirements of data processing and security protocols. Specifically, the random number rate generated by the QRNG is set to ≥100Mbps to ensure high-speed and efficient data processing, meeting the high-frequency requirements of modern data transmission.

[0221] In some embodiments, the generation process of the high entropy random number generator can be implemented in the following manner:

[0222] Generate random bit sequences through interference phenomena;

[0223] These bit sequences are converted into binary random numbers and then post-processed (such as hash function processing) to improve the quality of the random numbers.

[0224] These generated random numbers play a role in various modules of the system, such as key generation, random perturbation, encryption, etc., and their randomness and unpredictability can effectively resist traditional classical attacks and future high-complexity computing attacks.

[0225] To further enhance the system's resistance to high-complexity computational attacks, the system embeds a lattice-based digital signature in the verification credential generated during the distributed verification step. Lattice-based digital signatures are a public-key encryption technique based on the computational difficulties of lattices. They leverage the difficulty of lattice problems to ensure the security of the encryption system.

[0226] In this embodiment, the lattice-based signature generation process can be as follows:

[0227] In the process of generating the verification credential, the verification data (such as verification result, timestamp, system status summary, etc.) is combined with a pair of lattice-based keys;

[0228] The verification certificate is signed using public key cryptography to ensure the integrity and non-tamperability of the certificate.

[0229] The specific mathematical formalization is:

[0230] Π sig =Sign lattice (Π), where Π sig Represents a verification certificate with a grid signature; Sign lattice (·) represents a lattice-based signature function, and the LWE (learning with noise) problem is usually used to ensure the security of the encryption system against high-complexity attacks.

[0231] Lattice-based signatures are resistant to high-complexity attacks and can effectively prevent highly complex computational models from exploiting weaknesses in traditional public-key cryptography. Therefore, by embedding them into the credentials generated by distributed authentication, the security and integrity of the authentication process can be ensured, preventing the risk of tampering with the credentials during transmission or storage.

[0232] During the distributed verification process, the system effectively protects the verification information transmitted between nodes through the randomness provided by a high-entropy random number generator and the encryption protection of lattice-based digital signature technology. These protections, combined with the key generation mechanism and fragment verification in the previous entropy decomposition process, ensure the overall system's ability to withstand highly complex computational models.

[0233] Typically, random numbers generated by a high-entropy random number generator are used to dynamically generate verification keys and perturbation functions, thereby enhancing the unpredictability of the keystream and ensuring that the verification key cannot be reversed even under the threat of high-complexity attacks. In the generated verification credentials, lattice-based signature technology provides additional security, ensuring the authenticity and immutability of the verification credentials.

[0234] In this embodiment, the system achieves multiple levels of protection against high-complexity attacks through the integration of a high-entropy random number generator and lattice-based digital signature technology. The high-entropy random number generator ensures the high randomness and unpredictability of the system's internal keys and parameters, thereby enhancing the system's resistance to high-complexity attacks. The lattice-based digital signature ensures the immutability and long-term verifiability of the authentication credentials, providing strong anti-tampering protection.

[0235] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data shredding method based on a four-dimensional manifold chaotic dynamical system, characterized in that: The following steps are involved: System initialization: configure and activate the four-dimensional manifold chaotic dynamic system to generate a spatiotemporal coupled chaotic entropy source; Data classification processing: Identify the type characteristics of input data and dynamically select the corresponding entropy decomposition channel; Entropy decomposition processing: irreversibly compress the data according to the selected channel and output a set of fragments that meet the entropy value standard; Distributed verification: Collaborative verification of shard sets is performed through edge computing nodes to generate auditable verification credentials; Closed-loop update: The verification certificate is written into the blockchain, and the system chaos parameters are updated according to the stored results to form a feedback control closed loop.

2. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: The four-dimensional manifold chaotic dynamical system in the system initialization step is defined by the following fractional-order differential equations: Where α is the fractional differential order, σ is the spatial coupling coefficient, ρ is the nonlinear feedback coefficient, β is the dissipation coefficient, γ is the time tensor coupling strength, η is the time flow feedback coefficient, κ is the spatiotemporal synchronization coefficient, λ is the time flow attenuation coefficient, T is the local time flow variable, and W t is the time-dependent feature tensor.

3. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: The data classification processing step includes: Multimodal feature extraction: Text data: Calculate the average path length L of the dependency syntax tree avg ≥2.5; Image data: The spectral radius R of the extracted convolution feature map is ≥ 0.7; Video data: Calculate the fractal dimension D of the optical flow field f ≥1.8; Routing decision execution: The priority of logical operators is: first perform data type matching, then perform feature threshold judgment; L avg is the average path length of the dependency syntax tree, R is the spectral radius of the convolutional feature map, and D is the input data stream.

4. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: The entropy decomposition processing step includes: Constructing grammatical entanglement state; applying a decoherence gate operation; Irreversible fragments are generated through projection measurement, satisfying the entropy value ≤ 0.01 bit / word.

5. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: The entropy decomposition processing step includes, when selecting the image decomposition channel: Implementing metasurface optical convolution; Generate chaotic speckle patterns, satisfying the structural similarity index SSIM ≤ 0.05; Verify mutual information entropy.

6. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: The entropy decomposition processing step includes, when selecting the video decomposition channel: Constructing the space-time crystal Hamiltonian; Applying a critical temperature field triggers the phase transition; Output the annihilated video cube, satisfying PSNR ≤ 10dB.

7. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: In the distributed verification step: Key material required for collaborative verification: Based on the chaotic entropy source generated in the system initialization step, verification keys are generated in a time series; Generate auditable verification credential algorithm: The final verification credential is generated by collaboratively performing tensor operations on edge nodes and the cloud.

8. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: In the closed-loop update step: Blockchain evidence storage: The verification credentials generated by the distributed verification step are written to the blockchain and stored using an improved Merkle-Patricia tree structure; Parameter closed-loop update: Adjust the chaotic system parameters based on the stored evidence results.

9. The data shredding method based on a four-dimensional manifold chaotic dynamic system according to claim 1 is characterized in that: It also includes measures to resist highly complex attacks: Integrate a high entropy random number generator in the entropy decomposition process, with a generation rate of ≥100Mbps; Embed lattice-based digital signatures in the verification credentials of the distributed verification step.

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