A Method and System for Market Supervision Data Security Protection Based on Dynamic Encryption Strategy

Abstract

This invention discloses a method and system for protecting market supervision data based on a dynamic encryption strategy, relating to the field of information security technology. The method includes: generating an initial seed input to an improved four-dimensional hyperchaotic system, and generating a dynamic encryption parameter set through chaotic mapping iteration; adaptively fragmenting the market supervision data to be encrypted to generate a set of data sub-blocks; employing symmetric encryption based on a dynamic S-box for odd-indexed sub-blocks, and using chaotic stream encryption associated with the current dynamic subkey for even-indexed sub-blocks, and generating a unique quantum chaotic fingerprint for each encrypted sub-block; jointly constructing a searchable encrypted index using a post-quantum encryption algorithm and a lattice-based encryption algorithm for querying without decrypting the data; this invention not only significantly improves the encryption security, privacy protection capabilities, and resistance to quantum computing attacks of market supervision data, but also provides an efficient, flexible, and scalable data security protection scheme.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and in particular to a method and system for protecting market supervision data security based on dynamic encryption strategies. Background Technology

[0002] The market supervision data exchange platform carries important business data from market supervision departments and related agencies at all levels. This data includes enterprise registration information, regulatory enforcement records, complaint and reporting content, and credit rating data, all of which are highly sensitive and require strict confidentiality. However, existing platforms mostly employ traditional static encryption and access control methods, which, in the face of increasingly complex cybersecurity threats, still have the following shortcomings:

[0003] Static encryption is vulnerable to breaches: many current systems use fixed keys and algorithms (such as static S-box symmetric encryption) to protect data. If an attacker monitors or obtains the ciphertext over a long period, the unchanging encryption mechanism may be analyzed and cracked. Some studies have shown that introducing dynamically changing encryption elements can significantly improve security.

[0004] Key management remains a challenge for cryptographic algorithms, especially in large-scale systems where key generation, distribution, and storage are vulnerable to attacks. Traditional methods lack flexible key update and automated management mechanisms, resulting in data encryption protection failing to adapt to potential security threats in real time.

[0005] Privacy protection issues: With the continuous development of quantum computing and advanced computing capabilities, traditional encryption methods can no longer meet the needs of future information protection. In particular, when facing possible quantum computing attacks, existing encryption algorithms show weak resistance to quantum attacks and are unable to effectively protect data privacy.

[0006] Conflict between querying and data protection: Traditional encryption technologies often prevent efficient querying of encrypted data after encryption, or require exposing parts of the data during the query. While existing searchable encryption technologies provide the ability to query encrypted data, their security and efficiency are still subject to many limitations, and their ability to resist quantum attacks has not yet been effectively verified. Summary of the Invention

[0007] To address the above issues, this invention proposes a market supervision data security protection method and system based on dynamic encryption strategies. By introducing technologies such as dynamic encryption parameter generation mechanisms, quantum random number generators, chaotic mapping iterations, and dynamic key update mechanisms, it aims to provide a higher level of security protection while taking into account system availability and performance, thus meeting the dual requirements of market supervision operations for data security and data utilization.

[0008] The present invention achieves the above objectives through the following technical solutions:

[0009] A method for protecting market regulatory data security based on dynamic encryption strategies, the method comprising:

[0010] An initial seed is generated by a quantum random number generator and input into an improved four-dimensional hyperchaotic system. A dynamic encryption parameter set, including a dynamic subkey and a dynamic S-box, is generated through chaotic mapping iteration. At the same time, the entropy value of the dynamic encryption parameter set is monitored in real time. When the entropy value drops below a set threshold, the initial seed is automatically refreshed and a new dynamic encryption parameter set is generated iteratively.

[0011] Based on the dynamic encryption parameter set, the market supervision data to be encrypted is adaptively fragmented to generate a set of data sub-blocks. Odd-indexed sub-blocks in the data sub-block set are encrypted using symmetric encryption based on dynamic S-boxes, while even-indexed sub-blocks are encrypted using chaotic stream encryption associated with the current dynamic subkey. A unique quantum chaotic fingerprint is generated for each encrypted sub-block to verify the integrity and uniqueness of the encrypted data. A redundancy backup strategy is set according to the importance level of each encrypted sub-block.

[0012] For the query keywords submitted by the user, a searchable encrypted index is jointly constructed using a post-quantum encryption algorithm and a lattice-based encryption algorithm, which is used to perform query operations without decrypting the data. After the client submits a zero-knowledge proof for the server to verify the legality of the query, the server performs index retrieval based on chaotic topology rules, removes duplicates, and returns a set of encrypted result identifiers.

[0013] Preferably, the method for generating a dynamic encryption parameter set through chaotic mapping iteration includes:

[0014] An initial seed Q_Seed is generated using a multi-source quantum random number generator that integrates photon and charge sources.

[0015] The initial seed Q_Seed is used to generate multiple initial vectors for the chaotic system via hash expansion, and then reversibly expanded based on Gaussian random matrix mapping, combined with the platform master key for sensitive perturbation;

[0016] During the perturbation process, a nonlinear permutation masking mechanism is introduced to perform secondary obfuscation on the expanded initial vector;

[0017] The initial vector is evolved according to the following iterative relationship of the four-dimensional hyperchaotic mapping:

[0018]

[0019] In the formula, X(n), Y(n), Z(n), and W(n) are the four variables of the improved four-dimensional hyperchaotic system, and +1X(n+1), Y(n+1), Z(n+1), and W(n+1) are the updated variables; a, b, c, and d are adjustment parameters; and Φ is the quantum gated perturbation function. Indicates combination operations; H represents the XOR operation; H(·) is a lightweight hash compression function.

[0020] After a preset number of iterations, each chaotic trajectory is sampled to generate a dynamic subkey group and a dynamic S-box mapping table, which constitute a dynamic encryption parameter set P(t).

[0021] Preferably, the method for automatically refreshing the initial seed and iteratively generating a new set of dynamic encryption parameters when the entropy value drops below a set threshold includes:

[0022] For each round of generated dynamic encryption parameter set P(t), perform Shannon entropy H. Shannon , Renyi entropy H Renyi and sample entropy H Sample Multi-dimensional entropy joint evaluation to construct a comprehensive entropy value H total The formula is:

[0023] H total =w1·H Shannon +w2·H Renyi +w3·Norm(H Sample );

[0024] In the formula, w1, w2, and w3 are the entropy weights, and w1 + w2 + w3 = 1; Norm(H Sample ) indicates sample entropy normalization;

[0025] Configure a multi-level entropy response mechanism, including:

[0026] Safe range: the comprehensive entropy value H total If the value exceeds the preset first threshold, maintain the current parameter unchanged;

[0027] Warning interval: The comprehensive entropy value H total The adjustment parameters a, b, c, and d of the chaotic mapping are dynamically adjusted between a preset first threshold and a preset second threshold.

[0028] Danger zone: the comprehensive entropy value H total If the value falls below the preset second threshold, the initial seed Q_Seed refresh action is triggered.

[0029] Before performing a refresh action, the system dynamically adjusts the refresh frequency and the preset number of iterations based on the current number of concurrent accesses and data traffic load information of the platform.

[0030] When a refresh is triggered, the quantum random number generator is called again to generate a new initial seed, and a random fragment is selected from the dynamic encrypted parameter set P(t) whose entropy value is in the safe range in the previous round as a perturbation factor, which is combined with the new initial seed to generate an initial vector;

[0031] The refresh strategy is optimized based on a lightweight reinforcement learning model. The lightweight reinforcement learning model decides whether to use rehashing, gene transfer, or a complete restart mode based on the historical entropy trend and refresh effect feedback.

[0032] The new initial vector is subjected to chaotic iterative evolution, and the entropy value is verified for the generated result in multiple consecutive rounds. If the entropy value is lower than the safe range for two consecutive rounds, an enhanced restart mechanism is executed.

[0033] Preferably, the enhanced restart mechanism specifically includes:

[0034] Discard all current states;

[0035] Recall the quantum random number generator to generate a new initial seed;

[0036] Reset the initial vector of the chaotic map;

[0037] Increase the initial number of iterations;

[0038] Switch to the backup chaotic mapping system;

[0039] Until the overall entropy value of the dynamic encryption parameter set P(t) returns to the safe range.

[0040] Preferably, the method for adaptively sharding the market supervision data to be encrypted based on the dynamic encryption parameter set includes:

[0041] The comprehensive entropy value H extracted from the dynamic encryption parameter set P(t) total The number of segments N for market supervision data D is determined by the following formula:

[0042]

[0043] In the formula, DataSize(D) represents the size of the market supervision data D to be encrypted;

[0044] A pseudo-random mask sequence Mask(t) is generated based on the dynamic encryption parameter set P(t) to define the segmentation position of market supervision data D;

[0045] Using a pseudo-random mask sequence Mask(t), the market supervision data D to be encrypted is divided into a set of data sub-blocks {D} based on the mask change points. i The boundaries of each data block are determined by the mask value;

[0046] After partitioning, based on the chaotic trajectory π(t), the data sub-block set {D} is processed. i The logical order of the data blocks is disturbed; specifically, a chaotic perturbation function π is used to shuffle the physical order of the data sub-blocks and generate a new logical order {D}. π(i)}

[0047] Preferably, after encrypting the data sub-blocks, a quantum chaotic fingerprint based on the historical encryption chain is generated for each sub-block by combining the previously encrypted historical data and based on a multi-hash mechanism;

[0048] The entropy density is calculated for each data sub-block, and the importance of each data sub-block is evaluated based on the entropy density, and divided into three levels: high, medium and low.

[0049] Configure corresponding redundancy backup strategies based on the importance level of data sub-blocks, specifically as follows:

[0050] High-level: For critical and sensitive data, a triple-replica storage strategy is adopted, and an erasure coding mechanism based on XOR operation is superimposed. Each replica is stored on a different physical node, and redundant check blocks are generated through XOR encoding to ensure that in the event of a single node failure, the remaining replicas and check data can be used for rapid recovery.

[0051] Medium level: It adopts a dual-replica storage mechanism and adds a timestamp chain structure. The two replicas establish a hash association with an increasing timestamp to form an immutable time-series record. Any modification to a replica will trigger chain verification to prevent data tampering and historical version forgery.

[0052] Low level: Implements single-copy storage, uses only a lightweight hash algorithm to generate data fingerprints, and achieves basic integrity verification through hash values ​​with low computational overhead.

[0053] Preferably, the method for constructing a searchable encrypted index using a post-quantum encryption algorithm and a lattice-based encryption algorithm includes:

[0054] The query keyword Q is encrypted using a post-quantum encryption algorithm to obtain the encrypted query keyword E. PQE (Q);

[0055] The query keyword Q is encrypted using a lattice-based encryption algorithm to obtain the encrypted query keyword E. LWE (Q);

[0056] Merge encrypted query keywords to generate a searchable encrypted query index Index(Q), the expression is:

[0057]

[0058] In the formula, P(t) is the set of dynamic encryption parameters;

[0059] The client submits a query request to the server using the searchable encrypted query index (Q).

[0060] Preferably, after the client submits a zero-knowledge proof for the server to verify the query's validity, the server performs an index retrieval based on chaotic topology rules, removes duplicates, and returns a set of encrypted result identifiers. The method includes:

[0061] After receiving the searchable encrypted query index Index(Q) and zero-knowledge proof ZKP(Q) submitted by the client, the server verifies the validity of the query;

[0062] The server generates chaotic topology rules T based on the current dynamic encrypted parameter set P(t). chaos (t), and according to the chaotic topology rule T chaos (t) Performs encrypted index retrieval to query the encrypted data set;

[0063] When returning query results, the server uses an intelligent deduplication mechanism to remove duplicate encrypted result identifiers through similarity evaluation and dynamically adjusts the returned content through fuzzy search technology to ensure that the query results are highly matched with user needs, while avoiding the leakage of data scale.

[0064] The client decrypts the deduplicated query results and verifies the integrity of the query results using quantum chaotic fingerprinting to ensure that the query results are consistent with historical versions of the encrypted data.

[0065] Preferably, the method further includes privacy-preserving computation and dynamic access control and authentication;

[0066] Specifically, the privacy-preserving calculation is as follows:

[0067] Layered encryption is applied to the encrypted data sub-blocks, and chaotic differential privacy noise based on the dynamic encryption parameter set P(t) is introduced during federated learning training to update the global model weights, as shown in the following formula:

[0068]

[0069] In the formula, W t+1 W represents the weights of the global model at time t+1. t η represents the weights of the global model at time t; η represents the learning rate. λ represents the gradient of the loss function; λ represents the control factor for differential privacy noise; ChaosNoise(P(t)) represents the chaotic differential privacy noise generated based on the dynamic encryption parameter set P(t);

[0070] The dynamic access control and authentication specifically refers to:

[0071] By combining user access behavior patterns, query frequency, and user roles, the access strategy is dynamically adjusted, and a post-quantum security authentication protocol is used for identity verification.

[0072] The combination of multi-factor authentication and post-quantum authentication protocol provides a multi-layered authentication mechanism to ensure a more secure authentication process. During communication, the post-quantum authentication protocol is used to verify the user's identity, ensuring the reliability of the user's identity and resistance to quantum attacks.

[0073] Master key management and rotation are performed by the hardware security module HSM based on the chaos enhancement mechanism. The chaotic master key and functional encryption key are stored hierarchically and replaced periodically to ensure the security of the keys and the randomness of the updates.

[0074] Logs are generated for all operations involving key generation, data encryption / decryption, querying, and analysis.

[0075] Regularly check the logs to detect abnormal behavior, including unusually frequent decryption requests and a large number of consecutive query hits, and issue timely alerts.

[0076] A market supervision data security protection system based on dynamic encryption strategies, the system comprising:

[0077] The dynamic encryption parameter generation module is used to generate an initial seed and input it into the improved four-dimensional hyperchaotic system, and generate a dynamic encryption parameter set through chaotic mapping iteration;

[0078] The entropy monitoring module is used to monitor the entropy changes of the dynamic encryption parameter set in real time. When the entropy drops below a set threshold, the initial seed is automatically refreshed and a new dynamic encryption parameter set is generated iteratively.

[0079] The dynamic encryption module is used to adaptively fragment the market supervision data to be encrypted based on the dynamic encryption parameter set, generate a set of data sub-blocks, and use symmetric encryption based on dynamic S-box for odd-indexed sub-blocks in the set of data sub-blocks, and use chaotic stream encryption associated with the current dynamic subkey for even-indexed sub-blocks. At the same time, a quantum chaotic fingerprint is generated for each encrypted sub-block to verify the integrity and uniqueness of the encrypted data.

[0080] The encrypted query index building module is used to jointly build a searchable encrypted index using post-quantum encryption algorithm and lattice-based encryption algorithm. After the client submits a zero-knowledge proof for the server to verify the legality of the query, the index is retrieved based on chaotic topology rules, and after removing duplicates, a set of encrypted result identifiers is returned.

[0081] The privacy-preserving computation module is used to apply hierarchical encryption processing to encrypted data sub-blocks, and introduces chaotic differential privacy noise based on dynamic encryption parameter set control during federated learning training to update global model weights and adjust noise intensity according to differential privacy noise control factor.

[0082] The dynamic access control and authentication module is used to dynamically adjust access policies based on user access behavior patterns, query frequency, and user roles. It uses a post-quantum security authentication protocol for authentication and combines multi-factor authentication to provide a multi-level authentication mechanism, ensuring a more secure authentication process.

[0083] The key management module is used by the hardware security module HSM based on the chaos enhancement mechanism to perform master key management and rotation, and to store and periodically replace the chaotic master key and functional encryption key in a hierarchical manner to ensure the security of the key and the randomness of the update.

[0084] The logging and security auditing module is used to log all operations involving key generation, data encryption and decryption, querying and analysis, and to periodically check the logs to detect abnormal behavior, including abnormally frequent decryption requests and a large number of consecutive query hits, and to issue timely alerts.

[0085] The data storage and backup module is used to set redundancy backup strategies based on the importance level of data sub-blocks, including triple-replica storage, dual-replica storage, and single-replica storage mechanisms.

[0086] The beneficial effects of this invention are as follows: By dynamically generating encryption parameter sets, it avoids the security vulnerabilities caused by fixed encryption modes in traditional encryption algorithms, improving the unpredictability and anti-attack capabilities of the encryption process. Especially in the face of quantum computing threats, it effectively prevents the encryption system from being cracked. The introduction of differential privacy noise based on chaotic mapping control during the encryption process effectively enhances privacy protection during data processing and analysis. A searchable encrypted index jointly constructed using post-quantum encryption algorithms and lattice-based encryption algorithms enables efficient query operations without decrypting the data. Compared with traditional encrypted query methods, the innovative chaotic topology rules and deduplication query mechanism significantly improve the efficiency and privacy protection of the query process. During encrypted data queries, it effectively prevents information leakage while improving the accuracy and relevance of query results. The hardware security module HSM based on a chaotic enhancement mechanism performs hierarchical storage and periodic rotation of keys. Combined with the dynamic characteristics of chaotic systems, the key update process is more flexible and random, improving the key's anti-attack capabilities and avoiding the risks of overly fixed and easily cracked keys present in traditional key management schemes. Furthermore, this invention further enhances the overall security and monitorability of the system by logging and auditing key generation, data encryption / decryption, and query operations. Attached Figure Description

[0087] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0088] in:

[0089] Figure 1 This is a flowchart of the method in an embodiment of the present invention;

[0090] Figure 2 This is a system structure block diagram in an embodiment of the present invention. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0092] like Figure 1 As shown, this is an embodiment of the present invention, which provides a method for protecting market supervision data security based on a dynamic encryption strategy, including the following:

[0093] S1: Quantum-enhanced chaotic parameter generation

[0094] An initial seed is generated by a quantum random number generator and input into an improved four-dimensional hyperchaotic system. Through chaotic mapping iteration, a dynamic encryption parameter set is generated, including a dynamic subkey and a dynamic S-box. At the same time, the entropy change of the dynamic encryption parameter set is monitored in real time. When the entropy drops below a set threshold (e.g., 7.8 bits / byte), the initial seed is automatically refreshed and a new dynamic encryption parameter set is generated iteratively.

[0095] In one embodiment, the method for generating a dynamic encryption parameter set through chaotic mapping iteration includes:

[0096] SA1: The initial seed Q_Seed is generated by a multi-source quantum random number generator that combines photon source and charge source. The photon source is responsible for providing high-speed, unpredictable physical random events, while the charge source provides an enhanced entropy basis based on thermal noise fluctuations. The combination of the two can enhance the anti-predictability and true randomness of seed generation.

[0097] SA2: The initial seed Q_Seed is expanded by hash (such as Keccak (SHA-3)) to generate multiple initial vectors for the chaotic system, and then reversibly expanded based on Gaussian random matrix mapping to distribute them in a high-dimensional state space, thereby improving the uniformity and untraceability of the initial state distribution. The expanded initial values ​​are further combined with the platform master key K0 to perform perturbation (such as XOR or cross mapping) to bind key sensitivity.

[0098] SA3: During the perturbation process, a nonlinear permutation masking mechanism is introduced, including operations such as nonlinear permutation, bit reversal, and non-uniform bit perturbation on the vector to simulate multiple nonlinear path perturbations and perform secondary confusion on the expanded initial vector, thereby increasing the complexity of the initial state of the system.

[0099] SA4: The initial vector is evolved according to the following four-dimensional hyperchaotic mapping iterative relationship:

[0100]

[0101] In the formula, X(n), Y(n), Z(n), and W(n) are the four variables of the improved four-dimensional hyperchaotic system, and +1X(n+1), Y(n+1), Z(n+1), and W(n+1) are the updated variables; a, b, c, and d are adjustment parameters; and Φ is the quantum gated perturbation function. Indicates combination operations; H(·) represents the XOR operation; H(·) is a lightweight hash compression function.

[0102] SA5: After completing a preset number of iterations (e.g., 10,000 rounds), sample each chaotic track to generate a dynamic subkey group and a dynamic S-box mapping table (sample and output the chaotic value sequence from the chaotic track at a set interval, sort the sequence according to the value size and renumber it to construct an 8-bit input → 8-bit output dynamic S-box, and select specific positions in the sequence to construct the dynamic subkeys required for multiple rounds of encryption), forming a dynamic encryption parameter set P(t), which is used for subsequent heterogeneous encryption operations on data sub-blocks.

[0103] Furthermore, based on the real-time entropy changes of the dynamic encryption parameter set, the adjustment parameters a, b, c, and d of the chaotic system are dynamically adjusted so that the system tends to evolve towards a higher degree of chaos when the entropy decreases. Local perturbation optimization strategies can also be introduced during the dynamic S-box generation process to improve the nonlinearity and anti-differential characteristics of the final replacement box by changing the order and permutation of the perturbation sampling values.

[0104] This embodiment greatly enhances the sensitivity and irreversibility of chaotic systems to initial conditions through a three-layer hybrid mechanism of multi-source quantum entropy input, Gaussian mapping extension, and nonlinear perturbation. The four-dimensional coupled hyperchaotic mapping structure has a higher Lyapunov exponent, increasing the uncertainty of the sequence. The real-time adjustable parameters and state entropy monitoring mechanism enable the generation process to have adaptive "self-healing" capabilities, solving the risk of weak entropy degradation in static systems. The generated S-box and subkey structure are based on time-series dynamic output, effectively resisting linear and differential attacks, while supporting the replacement of high-strength symmetric encryption algorithms (such as SM4, AES, etc.).

[0105] The core purpose of comprehensively judging multiple entropy values ​​is to assess both the quality of randomness (Shannon) and frequency skew (Renyi), while avoiding sample entropy. Therefore, simple averaging is insufficient; instead, a decision model based on a weighted confidence mechanism or a fusion rule-based judgment model should be used. An example method is as follows:

[0106] Define a minimum acceptable value (e.g., bit / byte) for each entropy:

[0107] H Shannon ≥7.85, H Renyi ≥7.8, H Sample ≥0.7 (unit is standardized output)

[0108] Define the judgment rules:

[0109] If all conditions are met, a high-entropy condition is met, and the system enters a safe zone.

[0110] Any interval falling into the suboptimal interval (e.g., H) Renyi (∈[7.5,7.8]) → Entering the warning interval;

[0111] If any item is significantly below the threshold, a low entropy check is performed, triggering a refresh.

[0112] This embodiment preferably employs a weighted entropy fusion scoring model. In one embodiment, when the entropy value drops below a set threshold, the initial seed is automatically refreshed and a new dynamic encryption parameter set is iteratively generated. The method includes:

[0113] SB1: Perform Shannon entropy H on the dynamically generated encryption parameter set P(t) in each round. Shannon , Renyi entropy H Renyi and sample entropy H Sample Multi-dimensional entropy joint evaluation to construct a comprehensive entropy value H total The formula is:

[0114] H total =w1·H Shannon +w2·H Renyi+w3·Norm(H Sample );

[0115] In the formula, w1, w2, and w3 are the entropy weights, and w1 + w2 + w3 = 1 (the default recommendation is w1 = 0.4, w2 = 0.4, w3 = 0.2); Norm(H Sample ) indicates sample entropy normalization (e.g., mapping to the [0,8] interval);

[0116] SB2: Define a multi-level entropy response mechanism, including:

[0117] Safe range: Comprehensive entropy value H total Higher than a preset first threshold (e.g., H) total >7.8), keep the current parameters unchanged;

[0118] Warning interval: Comprehensive entropy value H total Between the preset first threshold and the preset second threshold (e.g., H) total ∈[7.3,7.8]), dynamically adjust the adjustment parameters a, b, c, d of the chaotic mapping;

[0119] Danger zone: Comprehensive entropy value H total Below the preset second threshold (i.e., H) total <7.3), triggers the initial seed Q_Seed refresh action;

[0120] SB3: Before performing a refresh action, the system dynamically adjusts the refresh frequency and the preset number of iterations based on the current platform's concurrent access volume, data traffic, and other load information (optimization and adjustment before refresh to control refresh overhead and time window) to ensure that the refresh behavior achieves a balance between security and performance.

[0121] SB4: When a refresh is triggered, the quantum random number generator is called again to generate a new initial seed, and a random fragment is selected from the dynamic encrypted parameter set P(t) whose entropy value is in the safe range in the previous round as a perturbation factor. This fragment is then combined with the new initial seed to generate an initial vector (new initial conditions are generated during the refresh to prepare for a new round of evolution).

[0122] SB5: The refresh strategy is optimized based on a lightweight reinforcement learning model. The lightweight reinforcement learning model decides whether to use rehashing, gene transfer, or a complete restart mode based on the historical entropy trend and refresh effect feedback (after the new seed is generated, the reinforcement learning model decides which strategy to use to refine the initial state adjustment) in order to reduce false triggers and improve efficiency.

[0123] SB6: Perform chaotic iterative evolution on the new initial vector and verify the entropy value of the generated result for multiple consecutive rounds. If the entropy value is lower than the safe range for two consecutive rounds, then execute the enhanced restart mechanism (if the effect is still not good after refreshing, start a complete enhanced restart). This mechanism ensures that the generated dynamic encrypted parameter set P(t) always maintains high statistical randomness and unpredictability.

[0124] Furthermore, the enhanced restart mechanism specifically includes:

[0125] Discard all current states;

[0126] Recall the quantum random number generator to generate a new initial seed;

[0127] Reset the initial vector of the chaotic map;

[0128] Increase the initial number of iterations;

[0129] Switch to the backup chaotic mapping system;

[0130] Until the overall entropy value of the dynamic encryption parameter set P(t) returns to the safe range.

[0131] S2: Dynamic Fractal Encrypted Storage

[0132] The market supervision data to be encrypted is adaptively fragmented based on a dynamic encryption parameter set to generate a set of data sub-blocks. Odd-indexed sub-blocks in the data sub-block set are encrypted using symmetric encryption based on dynamic S-boxes, while even-indexed sub-blocks are encrypted using chaotic stream encryption associated with the current dynamic subkey. A unique quantum chaotic fingerprint is generated for each encrypted sub-block to verify the integrity and uniqueness of the encrypted data. A redundancy backup strategy is set according to the importance level of each encrypted sub-block.

[0133] Step S2 specifically includes:

[0134] S21: Extract the comprehensive entropy value H from the dynamic encryption parameter set P(t). total The number of segments N for market supervision data D is determined by the following formula:

[0135]

[0136] In the formula, DataSize(D) represents the size of the market supervision data D to be encrypted;

[0137] S22: Generate a pseudo-random mask sequence Mask(t) based on the dynamic encryption parameter set P(t) to define the segmentation position of the market supervision data D;

[0138] S23: Using the pseudo-random mask sequence Mask(t), the market supervision data D to be encrypted is divided into a set of data sub-blocks {D} based on the mask change points. i The boundaries of each data block are determined by the mask value;

[0139] S24: After partitioning, based on the chaotic trajectory π(t), the data sub-block set {D} is processed. i The logical order of the data blocks is disturbed; specifically, a chaotic perturbation function π is used to shuffle the physical order of the data sub-blocks and generate a new logical order {D}. π(i) This ensures that the physical storage order of data sub-blocks is not consistent with their logical order, thereby increasing the anti-analysis capability of encrypted data;

[0140] S25: For the set of data sub-blocks after perturbation, odd-indexed sub-blocks are encrypted using symmetric encryption based on dynamic S-box (such as SM4, AES), and even-indexed sub-blocks are encrypted using chaotic stream encryption associated with the current dynamic subkey (such as XOR encryption).

[0141] S26: Combining previous encrypted historical data, based on a multi-hash mechanism, a quantum chaotic fingerprint based on the historical encryption chain is generated for each sub-block to prevent data tampering and forgery;

[0142] S27: Calculate the entropy density for each data sub-block, and assess the importance of each data sub-block based on the entropy density, classifying them into three levels: high, medium, and low.

[0143] S28: Set the corresponding redundancy backup strategy according to the importance level of the data sub-blocks, specifically:

[0144] High-level: For critical and sensitive data (such as corporate financial and market analysis data), a triple-copy storage strategy is adopted, and an erasure coding mechanism based on XOR operation is superimposed. Each copy is stored on a different physical node, and redundant check blocks are generated through XOR encoding to ensure that in the event of a single node failure, the remaining copies and check data can be used for rapid recovery.

[0145] Medium level: For moderately sensitive data (such as product quality data and consumer feedback), a dual-copy storage mechanism is adopted, with an additional timestamp chain structure. A hash association with an increasing timestamp is established between the two copies to form an immutable time-series record. Any modification to a copy will trigger chain verification to prevent data tampering and historical version forgery.

[0146] Low-level: For low-sensitivity data (such as statistical data and public reports), single-copy storage is implemented. Only a lightweight hash algorithm is used to generate data fingerprints. Basic integrity verification is achieved through hash values ​​with low computational overhead (such as xxHash or BLAKE3), which reduces the consumption of storage and computing resources while ensuring basic protection capabilities.

[0147] S3: Quantum-resistant searchable encrypted query

[0148] For the query keywords submitted by the user, a searchable encrypted index is jointly constructed using a post-quantum encryption algorithm and a lattice-based encryption algorithm, which is used to perform query operations without decrypting the data. After the client submits a zero-knowledge proof for the server to verify the legality of the query, the server performs index retrieval based on chaotic topology rules, removes duplicates, and returns a set of encrypted result identifiers.

[0149] Specifically, the implementation method of step S3 includes:

[0150] S31: Client users input query keywords Q through the application interface, such as "company registration date" or "market product quality inspection report". These query keywords need to be kept private during transmission and should not be exposed to the server or other third parties. Therefore, this embodiment uses a post-quantum encryption algorithm to encrypt the query keyword Q, resulting in the encrypted query keyword E. PQE (Q), the query keyword Q is encrypted using a lattice-based encryption algorithm to obtain the encrypted query keyword E. LWE (Q), then merge the encrypted query keywords to generate a searchable encrypted query index Index(Q), the expression of which is:

[0151] Index(Q) = [E PQE (Q)⊕E LWE (Q)]⊕P(t);

[0152] In the formula, P(t) is a dynamic encryption parameter set to enhance the security of the query index and ensure consistency with the dynamic characteristics of the encryption system;

[0153] The client submits a query request to the server through the searchable encrypted query index (Q);

[0154] S32: In order to verify the legality of the query, the client uses the zero-knowledge proof (ZKP) protocol to generate a zero-knowledge proof ZKP(Q) for the corresponding query keyword Q. After receiving the searchable encrypted query index Index(Q) and zero-knowledge proof ZKP(Q) submitted by the client, the server verifies the legality of the query. The verification process ensures that the query content conforms to the system rules without exposing the actual query content.

[0155] S33: The server generates chaotic topology rules T based on the current dynamic encrypted parameter set P(t). chaos (t), this rule consists of a sequence generated by a chaotic system, used to guide the encrypted index retrieval operation of the query, according to the chaotic topology rule T. chaos(t) Perform encrypted index retrieval to query the encrypted data set; during the query process, the server does not decrypt any data, but only matches the encrypted query index with the encrypted database data;

[0156] S34: When returning query results, the server applies an intelligent deduplication mechanism to remove duplicate encrypted result identifiers through similarity evaluation (such as hash value matching, cosine similarity, etc.), ensuring that the returned query results are unique and relevant. It also dynamically adjusts the returned content through fuzzy search technology, so that the returned results are not only completely matched items, but also include highly relevant approximate matching items, increasing the flexibility of the query, ensuring that the query results are highly matched with the user's needs, and avoiding the leakage of data scale.

[0157] S35: The server generates an encrypted set of identifiers from the deduplicated query results. These identifiers do not contain any actual data, but are encrypted identifiers to ensure data privacy. The encrypted result identifier set is returned to the client through a secure encrypted channel. The client decrypts the deduplicated query results, recovers the data that matches the query conditions, and verifies the integrity of the query results using quantum chaotic fingerprinting to ensure that the query results are consistent with the historical versions of the encrypted data. If the data is tampered with or inconsistent, the client will receive a warning message and abort the operation.

[0158] S4: Privacy-Preserving Computing

[0159] The system divides the raw market supervision data into blocks, with each data sub-block set {D}. i All data undergoes layered encryption, with different encryption strategies employed at each layer to ensure data privacy and operability. For example, symmetric encryption algorithms can be used to encrypt certain sub-blocks, while asymmetric encryption strategies can be used for others, ensuring multi-layered data protection.

[0160] For certain data types (such as numerical data), SM4 or AES encryption can be used; while for sensitive information (such as user identity information), encryption based on public key infrastructure is used to ensure privacy protection at different levels.

[0161] Chaotic differential privacy noise, controlled by a dynamically encrypted parameter set P(t), is introduced during federated learning training to update the global model weights, as shown in the following formula:

[0162]

[0163] In the formula, W t+1 W represents the global model weights (or model parameters, i.e., the updated model weights) at time t+1. tη represents the global model weights at t (i.e., the current model weights); η represents the learning rate, a constant used to control the step size in each update, usually between 0 and 1. λ represents the gradient of the loss function, which measures the prediction error of the model under the current weights and guides the model on how to adjust its parameters to reduce the error; λ represents the control factor for differential privacy noise, which is used to adjust the intensity of privacy noise introduced by chaotic differential privacy noise.

[0164] ChaosNoise(P(t)) represents chaotic differential privacy noise generated based on a dynamically encrypted parameter set P(t). This noise is unpredictable and time-varying, designed to enhance the privacy protection capabilities of the model. By introducing chaotic noise, data leakage is prevented.

[0165] In each round of training, the learning rate η and the gradient of the loss function are used. The model weights are updated, and noise λ·ChaosNoise(P(t)) based on chaotic differential privacy is added to ensure privacy protection. The scheduling of noise intensity is handled by a chaotic scheduler, which dynamically adjusts the noise intensity according to different privacy requirements and changes in the attack model to optimize the privacy protection effect and improve the efficiency of the training process.

[0166] S5: Dynamic Access Control and Authentication

[0167] By combining user access behavior patterns, query frequency, and user roles, the access strategy is dynamically adjusted, and a post-quantum security authentication protocol is used for identity verification. This is the premise and entry point of the access control mechanism. The system needs to perceive information such as user behavior and role differences in order to make subsequent control strategy judgments.

[0168] The combination of multi-factor authentication and post-quantum authentication protocol provides a multi-layered authentication mechanism to ensure a more secure authentication process. During communication, the post-quantum authentication protocol is used to verify the user's identity, ensuring the reliability of the user's identity and resistance to quantum attacks.

[0169] Master key management and rotation are performed by the hardware security module HSM based on the chaos enhancement mechanism. The chaotic master key and functional encryption key are stored hierarchically and replaced periodically to ensure the security of the keys and the randomness of the updates (after authentication, a secure key management system is required to ensure data security).

[0170] Log all operations involving key generation, data encryption / decryption, querying, and analysis (the system should keep records of critical actions such as encryption and data retrieval to provide a basis for auditing);

[0171] Regularly check logs to detect abnormal behavior, including unusually frequent decryption requests and a large number of consecutive query hits, and issue timely alerts; log information is used for post-event auditing to identify potential security risks and respond promptly, making it a critical final link in the security closed loop.

[0172] like Figure 2 As shown, another embodiment of the present invention provides a security protection system for the market supervision data security protection method based on the dynamic encryption strategy described above, comprising:

[0173] The dynamic encryption parameter generation module is used to generate an initial seed and input it into the improved four-dimensional hyperchaotic system, and generate a dynamic encryption parameter set through chaotic mapping iteration;

[0174] The entropy monitoring module is used to monitor the entropy changes of the dynamic encryption parameter set in real time. When the entropy drops below the set threshold, the initial seed is automatically refreshed and a new dynamic encryption parameter set is generated iteratively.

[0175] The dynamic encryption module is used to adaptively fragment the market supervision data to be encrypted based on the dynamic encryption parameter set, generate a set of data sub-blocks, and use symmetric encryption based on dynamic S-box for odd-indexed sub-blocks in the set of data sub-blocks, and use chaotic stream encryption associated with the current dynamic subkey for even-indexed sub-blocks. At the same time, a quantum chaotic fingerprint is generated for each encrypted sub-block to verify the integrity and uniqueness of the encrypted data.

[0176] The encrypted query index building module is used to jointly build a searchable encrypted index using post-quantum encryption algorithm and lattice-based encryption algorithm. After the client submits a zero-knowledge proof for the server to verify the legality of the query, the index is retrieved based on chaotic topology rules, and after removing duplicates, a set of encrypted result identifiers is returned.

[0177] The privacy-preserving computation module is used to apply hierarchical encryption processing to encrypted data sub-blocks, and introduces chaotic differential privacy noise based on dynamic encryption parameter set control during federated learning training to update global model weights and adjust noise intensity according to differential privacy noise control factor.

[0178] The dynamic access control and authentication module is used to dynamically adjust access policies based on user access behavior patterns, query frequency, and user roles. It uses a post-quantum security authentication protocol for authentication and combines multi-factor authentication to provide a multi-level authentication mechanism, ensuring a more secure authentication process.

[0179] The key management module is used by the hardware security module HSM based on the chaos enhancement mechanism to perform master key management and rotation, and to store and periodically replace the chaotic master key and functional encryption key in a hierarchical manner to ensure the security of the key and the randomness of the update.

[0180] The logging and security auditing module is used to log all operations involving key generation, data encryption and decryption, querying and analysis, and to periodically check the logs to detect abnormal behavior, including abnormally frequent decryption requests and a large number of consecutive query hits, and to issue timely alerts.

[0181] The data storage and backup module is used to set redundancy backup strategies based on the importance level of data sub-blocks, including triple-replica storage, dual-replica storage, and single-replica storage mechanisms.

[0182] In summary, by introducing innovative technologies such as dynamic encryption strategies, post-quantum encryption algorithms, differential privacy protection, and multi-factor authentication, this invention not only significantly improves the encryption security, privacy protection capabilities, and resistance to quantum computing attacks of market supervision data, but also provides an efficient, flexible, and scalable data security protection solution.

[0183] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for protecting market supervision data security based on dynamic encryption strategies, characterized in that, The method includes: An initial seed is generated by a quantum random number generator and input into an improved four-dimensional hyperchaotic system. A dynamic encryption parameter set, including a dynamic subkey and a dynamic S-box, is generated through chaotic mapping iteration. At the same time, the entropy value of the dynamic encryption parameter set is monitored in real time. When the entropy value drops below a set threshold, the initial seed is automatically refreshed and a new dynamic encryption parameter set is generated iteratively. Based on the dynamic encryption parameter set, the market supervision data to be encrypted is adaptively fragmented to generate a set of data sub-blocks. Odd-indexed sub-blocks in the data sub-block set are encrypted using symmetric encryption based on dynamic S-boxes, while even-indexed sub-blocks are encrypted using chaotic stream encryption associated with the current dynamic subkey. A unique quantum chaotic fingerprint is generated for each encrypted sub-block to verify the integrity and uniqueness of the encrypted data. A redundancy backup strategy is set according to the importance level of each encrypted sub-block. For the query keywords submitted by the user, a searchable encrypted index is jointly constructed using a post-quantum encryption algorithm and a lattice-based encryption algorithm, which is used to perform query operations without decrypting the data. After the client submits a zero-knowledge proof for the server to verify the legality of the query, the server performs index retrieval based on chaotic topology rules, removes duplicates, and returns a set of encrypted result identifiers. The method also includes privacy-preserving computation and dynamic access control and authentication; Specifically, the privacy-preserving calculation is as follows: Layered encryption is applied to the encrypted data sub-blocks, and chaotic differential privacy noise based on the dynamic encryption parameter set P(t) is introduced during federated learning training to update the global model weights, as shown in the following formula: In the formula, W t+1 W represents the weights of the global model at time t+1. t η represents the weights of the global model at time t; η represents the learning rate. λ represents the gradient of the loss function; λ represents the control factor for differential privacy noise; ChaosNoise(P(t)) represents the chaotic differential privacy noise generated based on the dynamic encryption parameter set P(t); The dynamic access control and authentication specifically refers to: By combining user access behavior patterns, query frequency, and user roles, the access strategy is dynamically adjusted, and a post-quantum security authentication protocol is used for identity verification. The combination of multi-factor authentication and post-quantum authentication protocol provides a multi-layered authentication mechanism to ensure a more secure authentication process. During communication, the post-quantum authentication protocol is used to verify the user's identity, ensuring the reliability of the user's identity and resistance to quantum attacks. Master key management and rotation are performed by the hardware security module HSM based on the chaos enhancement mechanism. The chaotic master key and functional encryption key are stored hierarchically and replaced periodically to ensure the security of the keys and the randomness of the updates. Logs are generated for all operations involving key generation, data encryption / decryption, querying, and analysis. Regularly check the logs to detect abnormal behavior, including unusually frequent decryption requests and a large number of consecutive query hits, and issue timely alerts.

2. The market supervision data security protection method based on dynamic encryption strategy according to claim 1, characterized in that, The method for generating a dynamic encryption parameter set through chaotic mapping iteration includes: An initial seed Q_Seed is generated using a multi-source quantum random number generator that integrates photon and charge sources. The initial seed Q_Seed is used to generate multiple initial vectors for the chaotic system via hash expansion, and then reversibly expanded based on Gaussian random matrix mapping, combined with the platform master key for sensitive perturbation; During the perturbation process, a nonlinear permutation masking mechanism is introduced to perform secondary obfuscation on the expanded initial vector; The initial vector is evolved according to the following iterative relationship of the four-dimensional hyperchaotic mapping: In the formula, X(n), Y(n), Z(n), and W(n) are the four variables of the improved four-dimensional hyperchaotic system, X(n+1), Y(n+1), Z(n+1), and W(n+1) are the updated variables; a, b, c, and d are adjustment parameters; and Φ is the quantum gated perturbation function. Indicates combination operations; H represents the XOR operation; H(·) is a lightweight hash compression function. After a preset number of iterations, each chaotic trajectory is sampled to generate a dynamic subkey group and a dynamic S-box mapping table, which constitute a dynamic encryption parameter set P(t).

3. The market supervision data security protection method based on dynamic encryption strategy according to claim 2, characterized in that, The method for automatically refreshing the initial seed and iteratively generating a new set of dynamic encryption parameters when the entropy value drops below a set threshold includes: For each round of generated dynamic encryption parameter set P(t), perform Shannon entropy H. Shannon , Renyi entropy H Renyi and sample entropy H Sample Multi-dimensional entropy joint evaluation to construct a comprehensive entropy value H total The formula is: H total =w1·H Shannon +w2·H Renyi +w3·Norm(H Sample ); In the formula, w1, w2, and w3 are the entropy weights, and w1 + w2 + w3 = 1; Norm(H Sample ) indicates sample entropy normalization; Configure a multi-level entropy response mechanism, including: Safe range: the comprehensive entropy value H total If the value exceeds the preset first threshold, maintain the current parameter unchanged; Warning interval: The comprehensive entropy value H total The adjustment parameters a, b, c, and d of the chaotic mapping are dynamically adjusted between a preset first threshold and a preset second threshold. Danger zone: the comprehensive entropy value H total If the value falls below the preset second threshold, the initial seed Q_Seed refresh action is triggered. Before performing a refresh action, the system dynamically adjusts the refresh frequency and the preset number of iterations based on the current number of concurrent accesses and data traffic load information of the platform. When a refresh is triggered, the quantum random number generator is called again to generate a new initial seed, and a random fragment is selected from the dynamic encrypted parameter set P(t) whose entropy value is in the safe range in the previous round as a perturbation factor, which is combined with the new initial seed to generate an initial vector; The refresh strategy is optimized based on a lightweight reinforcement learning model. The lightweight reinforcement learning model decides whether to use rehashing, gene transfer, or a complete restart mode based on the historical entropy trend and refresh effect feedback. The new initial vector is subjected to chaotic iterative evolution, and the entropy value is verified for the generated result in multiple consecutive rounds. If the entropy value is lower than the safe range for two consecutive rounds, an enhanced restart mechanism is executed.

4. The market supervision data security protection method based on dynamic encryption strategy according to claim 3, characterized in that, The enhanced restart mechanism specifically includes: Discard all current states; Recall the quantum random number generator to generate a new initial seed; Reset the initial vector of the chaotic map; Increase the initial number of iterations; Switch to the backup chaotic mapping system; Until the overall entropy value of the dynamic encryption parameter set P(t) returns to the safe range.

5. The market supervision data security protection method based on dynamic encryption strategy according to claim 3, characterized in that, The method for adaptively fragmenting the market supervision data to be encrypted based on the dynamic encryption parameter set includes: The comprehensive entropy value H extracted from the dynamic encryption parameter set P(t) total The number of segments N for market supervision data D is determined by the following formula: In the formula, DataSize(D) represents the size of the market supervision data D to be encrypted; A pseudo-random mask sequence Mask(t) is generated based on the dynamic encryption parameter set P(t) to define the segmentation position of market supervision data D; Using a pseudo-random mask sequence Mask(t), the market supervision data D to be encrypted is divided into a set of data sub-blocks {D} based on the mask change points. i The boundaries of each data block are determined by the mask value; After partitioning, based on the chaotic trajectory π(t), the data sub-block set {D} is processed. i The logical order of the data blocks is disturbed; specifically, a chaotic perturbation function π is used to shuffle the physical order of the data sub-blocks and generate a new logical order {D}. π(i) } 6. The market supervision data security protection method based on dynamic encryption strategy according to claim 5, characterized in that, After encrypting the data sub-blocks, and combining them with the previous encryption history data, a quantum chaotic fingerprint based on the historical encryption chain is generated for each sub-block based on a multi-hash mechanism; The entropy density is calculated for each data sub-block, and the importance of each data sub-block is evaluated based on the entropy density, and divided into three levels: high, medium and low. Configure corresponding redundancy backup strategies based on the importance level of data sub-blocks, specifically as follows: High-level: For critical and sensitive data, a triple-replica storage strategy is adopted, and an erasure coding mechanism based on XOR operation is superimposed. Each replica is stored on a different physical node, and redundant check blocks are generated through XOR encoding to ensure that in the event of a single node failure, the remaining replicas and check data can be used for rapid recovery. Medium level: It adopts a dual-replica storage mechanism and adds a timestamp chain structure. The two replicas establish a hash association with an increasing timestamp to form an immutable time-series record. Any modification to a replica will trigger chain verification to prevent data tampering and historical version forgery. Low level: Implements single-copy storage, uses only a lightweight hash algorithm to generate data fingerprints, and achieves basic integrity verification through hash values ​​with low computational overhead.

7. The market supervision data security protection method based on dynamic encryption strategy according to claim 1, characterized in that, The method for constructing a searchable encrypted index by jointly using a post-quantum encryption algorithm and a lattice-based encryption algorithm includes: The query keyword Q is encrypted using a post-quantum encryption algorithm to obtain the encrypted query keyword E. PQE (Q); The query keyword Q is encrypted using a lattice-based encryption algorithm to obtain the encrypted query keyword E. LWE (Q); Merge encrypted query keywords to generate a searchable encrypted query index Index(Q), the expression is: In the formula, P(t) is the set of dynamic encryption parameters; The client submits a query request to the server using the searchable encrypted query index (Q).

8. The market supervision data security protection method based on dynamic encryption strategy according to claim 7, characterized in that, After the client submits a zero-knowledge proof for the server to verify the validity of the query, the server performs an index retrieval based on chaotic topology rules, removes duplicates, and returns a set of encrypted result identifiers. The method includes: After receiving the searchable encrypted query index Index(Q) and zero-knowledge proof ZKP(Q) submitted by the client, the server verifies the validity of the query; The server generates chaotic topology rules T based on the current dynamic encrypted parameter set P(t). chaos (t), and according to the chaotic topology rule T chaos (t) Performs encrypted index retrieval to query the encrypted data set; When returning query results, the server uses an intelligent deduplication mechanism to remove duplicate encrypted result identifiers through similarity evaluation and dynamically adjusts the returned content through fuzzy search technology to ensure that the query results are highly matched with user needs, while avoiding the leakage of data scale. The client decrypts the deduplicated query results and verifies the integrity of the query results using quantum chaotic fingerprinting to ensure that the query results are consistent with historical versions of the encrypted data.

9. The security protection system of the market supervision data security protection method based on dynamic encryption strategy as described in any one of claims 1-8, characterized in that, The system includes: The dynamic encryption parameter generation module is used to generate an initial seed and input it into the improved four-dimensional hyperchaotic system, and generate a dynamic encryption parameter set through chaotic mapping iteration; The entropy monitoring module is used to monitor the entropy changes of the dynamic encryption parameter set in real time. When the entropy drops below a set threshold, the initial seed is automatically refreshed and a new dynamic encryption parameter set is generated iteratively. The dynamic encryption module is used to adaptively fragment the market supervision data to be encrypted based on the dynamic encryption parameter set, generate a set of data sub-blocks, and use symmetric encryption based on dynamic S-box for odd-indexed sub-blocks in the set of data sub-blocks, and use chaotic stream encryption associated with the current dynamic subkey for even-indexed sub-blocks. At the same time, a quantum chaotic fingerprint is generated for each encrypted sub-block to verify the integrity and uniqueness of the encrypted data. The encrypted query index building module is used to jointly build a searchable encrypted index using post-quantum encryption algorithm and lattice-based encryption algorithm. After the client submits a zero-knowledge proof for the server to verify the legality of the query, the index is retrieved based on chaotic topology rules, and after removing duplicates, a set of encrypted result identifiers is returned. The privacy-preserving computation module is used to apply hierarchical encryption processing to encrypted data sub-blocks, and introduces chaotic differential privacy noise based on dynamic encryption parameter set control during federated learning training to update global model weights and adjust noise intensity according to differential privacy noise control factor. The dynamic access control and authentication module is used to dynamically adjust access policies based on user access behavior patterns, query frequency, and user roles. It uses a post-quantum security authentication protocol for authentication and combines multi-factor authentication to provide a multi-level authentication mechanism, ensuring a more secure authentication process. The key management module is used by the hardware security module HSM based on the chaos enhancement mechanism to perform master key management and rotation, and to store and periodically replace the chaotic master key and functional encryption key in a hierarchical manner to ensure the security of the key and the randomness of the update. The logging and security auditing module is used to log all operations involving key generation, data encryption and decryption, querying and analysis, and to periodically check the logs to detect abnormal behavior, including abnormally frequent decryption requests and a large number of consecutive query hits, and to issue timely alerts. The data storage and backup module is used to set redundancy backup strategies based on the importance level of data sub-blocks, including triple-replica storage, dual-replica storage, and single-replica storage mechanisms.

Images