Data security transmission method

Abstract

The invention discloses a secure data transmission method. The method comprises the following steps: acquiring a user attribute set, an access strategy, to-be-transmitted data and a system context as multi-source input data; determining a first chaotic initial parameter for encryption and a second chaotic initial parameter for hash enhancement based on the multi-source input data through a unified chaotic parameter generation mechanism; driving the LSL composite chaotic sequence to iterate by using the first chaotic initial parameter to generate a first chaotic sequence; generating a system master key and a public parameter based on the bilinear group and by using the first chaotic sequence, and generating a corresponding user private key based on the user attribute set; and driving the LSL composite chaotic sequence to iterate by using a second chaotic initial parameter to generate a second chaotic sequence. According to the method provided by the invention, chaos randomness enhancement, fine-grained dynamic access control, cross-layer integrity binding and end-to-end verifiable secure transmission are realized, and confidentiality, integrity and controllability in data circulation are improved.

Description

Technical Field

[0001] This invention relates to the field of data security technology, and in particular to a method for secure data transmission. Background Technology

[0002] As a new type of production factor, the secure and efficient circulation and sharing of data is the core foundation for the development of the digital economy. In the process of market-based allocation of data factors, how to achieve secure data transmission, fine-grained access control, and end-to-end integrity protection in an open network environment has become a critical and severe technical challenge.

[0003] Currently, secure data transmission primarily relies on a combination of transport layer security protocols (such as TLS / SSL) and traditional encryption algorithms (symmetric or asymmetric). This approach has systemic limitations: firstly, it lacks fine-grained dynamic access control mechanisms linked to the data content itself or user identity attributes, failing to achieve precise data usage control with "encrypt once, decrypt on demand"; secondly, data integrity protection often employs simple hash verification or post-hoc digital signatures, whose verification process is independent of encryption and access control procedures, making it difficult to detect data or policy tampering before decryption and unable to handle replay attacks. To implement access control, role-based access control (RBAC) or attribute-based access control (ABAC) models are widely used. However, traditional ABE (attribute-based encryption) schemes face significant bottlenecks in large-scale, dynamic scenarios: key management overhead increases dramatically with the number of users and attributes; changes in user attributes often require a full re-issuance of keys, resulting in inefficiency and difficulty in supporting frequent dynamic permission adjustments.

[0004] Regarding the fundamental sources of randomness in constructing the aforementioned security mechanisms, existing solutions largely rely on traditional pseudo-random number generators or single chaotic systems. The former may suffer from security vulnerabilities such as periodicity and predictability; the latter (e.g., a single Logistic mapping) is limited by a narrow range of chaotic parameters, finite sequence periods, and less-than-ideal statistical properties, making it difficult to provide random numbers of sufficient quality for high-security encryption and verification. Furthermore, in the construction of integrity verification data structures (such as Merkle trees), standard hash functions lack dynamic randomness injection, making verification paths potentially predictable or susceptible to collision attacks.

[0005] A more fundamental problem is that existing technology systems often employ a "stacked" or "pieced-together" design approach, simply combining modules such as random number generation, attribute encryption, and integrity verification as independent components. This model results in a disconnect between different security layers (confidentiality, access control, and integrity), lacking deep internal coordination and unified binding. Attackers may exploit these gaps to launch tampering or replay attacks targeting a single layer. Simultaneously, the overall system scalability is insufficient, making it difficult to adapt to the high concurrency, low latency, and dynamically changing application requirements of data flow.

[0006] Therefore, there is an urgent need in this field for a new type of secure transmission and usage control method that can integrate high-quality random number generation, fine-grained dynamic attribute access control, and strong tamper-resistant integrity verification, and deeply integrate these capabilities through innovative architecture design to form an internally unified, collaborative, and end-to-end verifiable security protection system. Summary of the Invention

[0007] To address the technical problems existing in the background art, this invention proposes a data security transmission method.

[0008] The present invention proposes a data security transmission method, comprising: Obtain user attribute sets, access policies, data to be transmitted, and system context as multi-source input data; Based on multi-source input data, a unified chaotic parameter generation mechanism is used to determine the first chaotic initial parameter for encryption and the second chaotic initial parameter for hash enhancement. The first chaotic sequence is generated by iterating the LSL composite chaotic sequence using the first chaotic initial parameters. Based on the bilinear group and utilizing the first chaotic sequence, the system master key and public parameters are generated, and the corresponding user private key is generated based on the user attribute set. The second chaotic sequence is generated by iterating the LSL composite chaotic sequence using the second chaotic initial parameters. Random numbers are provided using the first chaotic sequence, and attribute-based encryption is performed on the data to be transmitted based on the access policy and common parameters to generate a set of ciphertexts. The second chaotic sequence is used to derive dynamic salt values ​​for the data blocks after the data to be transmitted is divided into blocks, and a chaotic enhanced Merkle tree is constructed based on the dynamic salt values ​​to obtain the root hash and the verification path of each data block. Calculate the policy digest of the access policy, and fuse the policy digest with the root hash and the bridging material derived from the first chaotic sequence and the second chaotic sequence to generate additional authentication data; Using the key material derived from the first chaotic sequence, a message authentication code is calculated on the ciphertext set and the additional authentication data to obtain a cross-layer security binding tag; The encrypted set, verification path, policy digest, root hash, additional authentication data, and cross-layer security binding label are assembled into a secure data packet and sent.

[0009] Preferably, the step of determining the first chaotic initial parameter for encryption and the second chaotic initial parameter for hash enhancement based on multi-source input data through a unified chaotic parameter generation mechanism specifically includes: For attribute-based encryption scenarios, the user attribute set and access policy are obtained, serialized and merged into first structured data, and the hash value of the first structured data is calculated as the first fused hash. Multiple integers are extracted from the first fusion hash and input into the bitwise operation enhanced transformation function for calculation. The calculation result is normalized and mapped to the first chaotic initial parameters. For Merkle tree verification scenarios, the data content, node information, and system context are obtained, serialized, and merged into a second structured data. The hash value of this second structured data is then calculated as the second fused hash. Multiple integers are extracted from the second fusion hash and input into the bitwise operation-enhanced transformation function for calculation. The calculation result is then normalized and mapped to the second chaotic initial parameters.

[0010] Preferably, the bitwise operation enhancement transformation function is specifically: ; in, This indicates a bitwise enhanced transformation function. and This represents two randomly extracted 32-bit integers from the fused hash value generated by the SHA256 hash function from specific inputs in three scenarios. This represents the XOR operation. This indicates a modulo operation.

[0011] Preferably, the mathematical model of the LSL composite chaotic sequence is: ; in, Indicates the first The chaotic state value of the step; This represents the control parameters for the Logistic mapping. and This represents the first two chaotic state values.

[0012] Preferably, the step of using the first chaotic initial parameters to drive the iteration of the LSL composite chaotic sequence to generate the first chaotic sequence specifically includes: Receive a first chaotic initial parameter, which includes at least a first initial state value, a second initial state value, and control parameters; The first and second initial state values ​​are set as the first two initial states of the LSL composite chaotic sequence, and the control parameters are input into the mathematical model of the LSL composite chaotic sequence. Based on the mathematical model of the LSL composite chaotic sequence, the first two initial states are used as inputs to iteratively calculate the subsequent chaotic state values, and the chaotic state values ​​generated in each iteration are used as sequence elements. The iterative calculation process is executed continuously, and the generated chaotic state values ​​are arranged in order to form the first chaotic sequence, which is used for subsequent encryption key generation and random number extraction.

[0013] Preferably, the step of generating the system master key and public parameters based on a bilinear group and utilizing a first chaotic sequence, and generating the corresponding user private key based on the user attribute set, specifically includes: Choose a safety parameter and generate bilinear groups and their generators with the same prime order, while defining a bilinear mapping; Three random numbers are extracted sequentially from the first chaotic sequence and used as the first random component, the second random component, and the third random component, respectively, to form the system master key. Based on the system master key and the generator of the bilinear group, calculate the common parameters that generate multiple group elements; Each attribute string in the user attribute set is mapped to a corresponding group element using a hash function; Extract a master random number sequentially from the first chaotic sequence, and extract a corresponding attribute random number for each attribute in the user attribute set; Based on the system master key, master random number, attribute random number, and group elements mapped by each attribute, calculate and generate the master key component and multiple attribute key components of the user's private key.

[0014] Preferably, the step of using the second chaotic initial parameters to drive the iteration of the LSL composite chaotic sequence to generate the second chaotic sequence specifically includes: Receive a second chaotic initial parameter, which includes at least a third initial state value, a fourth initial state value, and corresponding control parameters; The third and fourth initial state values ​​are set as the first two initial states of the LSL composite chaotic sequence, and the corresponding control parameters are input into the mathematical model of the LSL composite chaotic sequence. Based on the LSL composite chaotic sequence mathematical model, two initial states are used as inputs to iteratively calculate subsequent chaotic state values, and the chaotic state values ​​generated in each iteration are used as sequence elements. The iterative calculation process is executed continuously, and the generated multiple chaotic state values ​​are arranged in sequence to form a second chaotic sequence, which is used to generate dynamic salt values ​​for the data blocks after the data to be transmitted is divided into blocks.

[0015] Preferably, the step of using a first chaotic sequence to provide random numbers and performing attribute-based encryption on the data to be transmitted according to the access strategy and common parameters to generate a ciphertext set specifically includes: The access strategy is defined as an access structure consisting of an access matrix and a row-to-attribute mapping function; The data to be transmitted is divided into blocks to obtain multiple data blocks; Extract a master random number sequentially from the first chaotic sequence, and extract the corresponding row random number and column random number sequentially for each row of the access matrix; Based on public parameters, the system master key, and the extracted master random number, the original message of each data block is calculated to generate the message ciphertext component corresponding to each data block. Based on common parameters and master random numbers, generate master ciphertext components corresponding to each data block; For each row of the access matrix, based on the attribute hash value corresponding to that row, the extracted row random number and column random number, a pair of row ciphertext components corresponding to each data block is generated; The message ciphertext component, the main ciphertext component, and the row ciphertext components corresponding to each data block are combined to form the complete ciphertext of that data block. The ciphertexts of all data blocks together form a ciphertext set.

[0016] Preferably, the step of deriving dynamic salt values ​​for data blocks after the data to be transmitted is divided into blocks using a second chaotic sequence, and constructing a chaotic-enhanced Merkle tree based on the dynamic salt values ​​to obtain the root hash and the verification path of each data block, specifically includes: The data to be transmitted is divided into blocks to obtain multiple data blocks; Based on the second chaotic sequence and the content and node context of each data block, a unique dynamic salt value is derived for each data block through a deterministic salt function; Each data block is combined with its corresponding dynamic salt value and then input into a standard hash function to calculate the enhanced hash value of each leaf node. Based on the second chaotic sequence and the context of each pair of child nodes, a dynamic salt value is derived for each internal node through a deterministic salt function. The hash values ​​of the left and right child nodes of the internal node are combined with the salt value and then input into a standard hash function. The enhanced hash values ​​of all internal nodes are obtained by iterative calculation from bottom to top. The hash value of the top-level node obtained from the final iteration is used as the root hash of the chaos-enhanced Merkle tree; The leaf node corresponding to each data block records the hash values ​​and position information of all necessary sibling nodes on the path from its own hash value to the root hash, forming the verification path of the data block.

[0017] Preferably, when constructing the chaos-enhanced Merkle tree, the calculation formulas for the leaf node hash and the internal node hash are as follows: ; ; in, For the first One data block, For dynamic salt values ​​derived from deterministic salt functions; , The hash value of the leaf node; The hash value of the internal node; , These are the hash values ​​of the left and right child nodes, respectively. The dynamic salt value of this node is derived from the deterministic salt function. Represents a standard hash function. This indicates a join operation.

[0018] Preferably, the deterministic salt function operates as follows: based on the second chaotic sequence, the data block content, and the node context, a dynamic salt value is generated by iterating the LSL composite chaotic sequence and extracting values.

[0019] Preferably, the calculation formula for generating additional authentication data is as follows: ; in, For additional authentication data; A summary of the access policy; For the root hash of the Merkle tree, Represents a standard hash function. Indicates a connection operation; For the first chaotic sequence With the second chaotic sequence Exported bridging material.

[0020] Preferably, the step of using key material derived from the first chaotic sequence to calculate a message authentication code on the ciphertext set and additional authentication data to obtain a cross-layer security binding tag specifically includes: Using the first chaotic sequence as the main input, it is processed through a predetermined key derivation function to derive key material for authentication. The ciphertext set is combined with additional authentication data to form the message to be authenticated; The derived key material is used to perform a message authentication code algorithm calculation on the message to be authenticated; The output of the message authentication code algorithm is used as the final cross-layer security binding label.

[0021] Preferably, it also includes a secure data packet reception and decryption step: Receive secure data packets and reconstruct the first and second chaotic sequences based on the same multi-source input data as the sender; Chaos-enhanced Merkle tree integrity verification is performed using the reconstructed second chaotic sequence and the verification path and root hash in the security packet; After the integrity verification is passed, the reconstructed bridging material and the first chaotic sequence are used to verify the cross-layer security binding tag. After the binding tag is successfully verified, the encrypted set is decrypted using the user's private key held by the receiving end to recover the data to be transmitted.

[0022] This invention proposes a data security transmission method that constructs a unified and collaborative security protection system by deeply integrating a chaotic enhancement mechanism, attribute-based encryption, and Merkle tree verification. First, an innovative LSL composite chaotic sequence provides a high-quality, unpredictable randomness foundation for the entire system, enhancing the security of key generation and encryption processes. Second, it achieves fine-grained, dynamic access control, supporting precise attribute-based permission management and efficient incremental updates, improving the flexibility and efficiency of policy adaptation. Third, it creates a cross-layer security binding mechanism that deeply integrates access control, data integrity, and confidentiality verification, ensuring that any single point of tampering is systematically identified and intercepted before decryption, forming a defense-in-depth capability. Finally, it provides an end-to-end, verifiable secure transmission framework that effectively resists complex threats such as man-in-the-middle attacks and replay attacks, comprehensively enhancing the system's reliability and robustness. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the workflow of a data security transmission method proposed in this invention. Figure 2 This is a schematic diagram of the encryption process of one embodiment of the data security transmission method proposed in this invention; Figure 3 This is a schematic diagram of the decryption process for one embodiment of the data security transmission method proposed in this invention; Figure 4 LSL composite chaotic sequence bifurcation diagram as one embodiment of the data secure transmission method proposed in this invention. ; Figure 5 LSL composite chaotic sequence bifurcation diagram as one embodiment of the data secure transmission method proposed in this invention. ; Figure 6 Lyapunov exponent diagram of LSL composite chaotic sequence for a data secure transmission method proposed in this invention. ; Figure 7 LSL composite chaotic sequence 0-1 test diagram as an embodiment of the data secure transmission method proposed in this invention. ; Figure 8 A sensitivity test diagram of an LSL composite chaotic sequence for one embodiment of a data secure transmission method proposed in this invention. ; Figure 9 A sensitivity test diagram of an LSL composite chaotic sequence for one embodiment of a data secure transmission method proposed in this invention. ; Figure 10 A sensitivity test diagram of an LSL composite chaotic sequence for one embodiment of a data secure transmission method proposed in this invention. ; Figure 11 A sensitivity test diagram of an LSL composite chaotic sequence for one embodiment of a data secure transmission method proposed in this invention. . Detailed Implementation

[0024] Reference Figures 1-11 The present invention proposes a data security transmission method, comprising the following steps: S1. Obtain user attribute set, access policy, data to be transmitted and system context as multi-source input data.

[0025] S2. Based on multi-source input data, a unified chaotic parameter generation mechanism is used to determine the first chaotic initial parameter for encryption and the second chaotic initial parameter for hash enhancement.

[0026] In this embodiment, step S2 specifically includes: for the attribute-based encryption scenario, obtaining the user attribute set and access policy, serializing and fusing them into first structured data, and calculating the hash value of the first structured data as the first fused hash; extracting multiple integers from the first fused hash and inputting them into a bitwise operation enhanced transformation function for calculation, and normalizing the calculation result to map it into a first chaotic initial parameter; for the Merkle tree verification scenario, obtaining the data content, node information and system context, serializing and fusing them into second structured data, and calculating the hash value of the second structured data as the second fused hash; extracting multiple integers from the second fused hash and inputting them into a bitwise operation enhanced transformation function for calculation, and normalizing the calculation result to map it into a second chaotic initial parameter.

[0027] Specifically, the bitwise operation enhancement transformation function is as follows: ; in, This indicates a bitwise enhanced transformation function. and This represents two randomly extracted 32-bit integers from the fused hash value generated by the SHA256 hash function from specific inputs in three scenarios. This represents the XOR operation. This indicates a modulo operation.

[0028] In this embodiment, a unified chaotic parameter generation mechanism is designed. Through multi-source data fusion and bitwise operation-enhanced transformation, corresponding chaotic sequences are generated for three different application scenarios. This mechanism ensures that different input combinations correspond to unique chaotic sequences, providing a high-quality randomness foundation for attribute-based encryption, Merkle tree verification, and random salt generation.

[0029] (1) Unified mechanism for generating chaotic parameters Chaotic parameter generation employs a unified four-step process: data preprocessing, information serialization, fused data construction, and chaotic parameter calculation. Bitwise operations are used to enhance the transformation function, converting multi-source data into initial parameters for the chaotic system.

[0030] (2) Generation of chaotic parameters in three application scenarios Scenario 1: Attribute-based encryption of chaotic parameters In attribute-based encryption scenarios, chaotic systems are used to enhance the randomness and unpredictability of encryption keys. The user attribute set and access policy are fused, and non-numerical attribute information is transformed into initial parameters of the chaotic system through mathematical transformations. In the data preprocessing stage, user attributes are converted into standardized string representations, and access policies are sorted and serialized. In the fused data construction stage, the serialized attribute set and policy set are combined, and a fused hash value is generated using the SHA256 hash function. In the chaotic parameter calculation stage, based on the fused hash value, bitwise operations are used to enhance the transformation and calculate the initial parameters of the chaotic system. ; ; ; in, and These represent the two initial state values ​​of the chaotic system. Represents the control parameters of a chaotic system. , , , This represents the four 32-bit integers extracted from the merged hash value. This represents a 32-bit integer extracted from the merged hash value. This represents a bitwise enhanced transform function. This parameter is used to initialize the LSL composite chaotic sequence, iteratively generating the first chaotic sequence. Used for attribute-based encryption.

[0031] Scenario 2: Merkle tree verification of chaotic parameters In the Merkle tree verification scenario, a chaotic system is used to enhance the randomness and unpredictability of the hash function. The data content hash value, node information, and system context fingerprint (which can be reconstructed) are fused to generate verification parameters with chaotic characteristics. In the data preprocessing stage, the Merkle tree data blocks are hashed using SHA256, and node-related information is standardized. In the fused data construction stage, the data content digest, node information, and system context fingerprint are fused, serialized into JSON, and then hashed using the SHA256 hash function to generate a fused hash value. The system context fingerprint is obtained by selecting reconstructable context fields (such as protocol version, domain label, implementation identifier, hash algorithm name, and other stable information), normalizing them, serializing them in a fixed key order, and then calculating their hash as the context fingerprint. This fingerprint is independent of instantaneous quantities, and the receiving end can recalculate it based on the same public context.

[0032] The chaotic parameter calculation phase uses fused hash values ​​and bitwise operations to enhance transformations to calculate Merkle trees to verify the initial parameters of the chaotic system. ; ; ; in, and These represent the two initial state values ​​of the chaotic system verified by the Merkle tree. This indicates that the Merkle tree is used to verify the control parameters of a chaotic system. , , , , This represents a set of 32-bit integers extracted from the merged hash value. This represents a bitwise enhanced transform function. This parameter is used to initialize the LSL composite chaotic sequence, and then iteratively generates a second chaotic sequence. Used for Merkle tree verification. This mechanism ensures that chaotic parameters are closely related to specific data content, node information, and system context fingerprints.

[0033] Scenario 3: Generating Chaotic Parameters with Random Salt Values In the random salt value generation scenario, a chaotic system is used to generate a unique random identifier for each data block, preventing rainbow table attacks and replay attacks. The data content hash, data metadata, and a deterministic seed (derived from the content hash and metadata) are fused to generate a dynamic salt value parameter with chaotic characteristics. During data preprocessing, the data content to be processed undergoes SHA256 hash calculation, parsing the hash value into several 32-bit integers. A bitwise operation enhancement transformation stage performs bitwise operation enhancement transformations on the parsed 32-bit integers, including combinations of XOR, modulo, and addition operations, enhancing the nonlinear characteristics of the values. The deterministic seed is derived from reconstructable data metadata (such as data length, type identifier, encoding method, and block partitioning strategy), which is normalized together with the data content hash and serialized in a fixed key order. The hash is then calculated as the deterministic seed. This seed does not depend on instantaneous quantities, and the receiving end can recalculate it based on the same packet information and public context.

[0034] The chaotic parameter calculation stage uses bitwise operations to enhance the transformation results, calculates the chaotic system parameters, and generates random salt values. ; ; ; in, and These represent the two initial state values ​​of a chaotic system generated by random salt values. This represents the control parameters of a chaotic system where random salt values ​​are generated. , , , , This represents a set of 32-bit integers parsed from the SHA256 hash value of the data content. This indicates a bitwise enhanced transformation function. This represents modulo operations. Implementation notes: In the salt value scenario, the engineering implementation can be determined using only four 32-bit integers after normalization transformation. and and inherit external context or preset parameters as Alternatively, the aforementioned five-element value scheme can be used; both are equivalent in parameter range and determinism. Using these parameters, the LSL composite chaotic sequence is initialized, generating eight chaotic values ​​through eight iterations. These values ​​are then converted into hexadecimal strings to form a 16-character deterministic salt value sequence. .

[0035] (3) Unified random number extraction method When extracting random numbers from chaotic sequences, the following unified method is used: ; in, This represents the extracted random number. Represents the first in the chaotic sequence elements ( This is the starting index of the chaotic sequence. (The offset relative to the starting position). Indicates rounding down. Modulo operation is represented. The modulus is used to convert floating-point values ​​to 32-bit integers. This method converts floating-point values ​​in a chaotic sequence into 32-bit random integers, and this method will be used for random number extraction in subsequent sections.

[0036] S3. Use the first chaotic initial parameters to drive the LSL composite chaotic sequence iteration to generate the first chaotic sequence.

[0037] In this embodiment, the mathematical model of the LSL composite chaotic sequence is: ; in, Indicates the first The chaotic state value of the step; This represents the control parameters for the Logistic mapping. and This represents the first two chaotic state values.

[0038] It should be noted that the mathematical model derivation of the LSL composite chaotic sequence includes: (1) Basic construction of Lucas recursion The Lucas recurrence relation is a recurrence rule that uses the sum of the previous two terms as the current term. In LSL composite chaotic sequences, the Lucas recurrence relation is defined as follows: ; in, This represents the Lucas recursive value at step n. and These represent the first two chaotic state values. This recursive relationship provides short-term dependencies in the sequence, enabling the sequence evolution to exhibit rich dynamic characteristics and memory effects.

[0039] (2) Introduction of the chaotic properties of the Logistic mapping The Logistic mapping is one of the most classic mappings in the study of chaotic systems. The traditional Logistic mapping... It exhibits chaotic characteristics within a certain range, and its value range is limited to... In the LSL composite chaotic sequence, the intermediate values ​​obtained by Lucas recursion are substituted into the Logistic mapping: ; in, Indicates the first The Logistic mapping results of the steps, This represents the control parameters for the Logistic mapping. This represents the Lucas recursive value. This mapping performs a nonlinear transformation on the Lucas recursive value using the Logistic function, introducing chaotic dynamics.

[0040] (3) Output modulation and boundary control of Sine mapping The result from the previous step is modulated using a sine map: ; in, Indicates the first The chaotic state value of the step. Pi is a constant. Represents the sine function. This represents the result of the Logistic mapping. The mapping implements three important functions: ensuring that the output sequence is strictly limited to a certain value. Within a certain range, it enhances the nonlinear properties of sequences, smooths sequences, reduces mutations, and improves the practicality of sequences.

[0041] By integrating the above steps, we can obtain the mathematical model of the LSL composite chaotic sequence.

[0042] It should be noted that the mathematical model of this LSL composite chaotic sequence implements a triple fusion mechanism, which has the following advantages: (1) Parameter constraints and characteristics. Chaotic control parameters Sequence range: This is guaranteed by the Sine mapping; (2) Chaos. The Logistic term provides the basic chaotic dynamics through the parameters. Controlling the degree of chaos; (3) Dual memory effect. Lucas recursion provides short-term memory for the sequence frame, while Sin memory terms provide additional long-term memory effects; (4) Boundedness: The output is constant. Within its range, it is suitable for direct use in encryption transformation.

[0043] In this embodiment, step S3 specifically includes: receiving a first chaotic initial parameter, which includes at least a first initial state value, a second initial state value, and a control parameter; setting the first and second initial state values ​​as the first two initial states of the LSL composite chaotic sequence, and inputting the control parameter into the mathematical model of the LSL composite chaotic sequence; based on the mathematical model of the LSL composite chaotic sequence, using the first two initial states as input, iteratively calculating subsequent chaotic state values, and using the chaotic state values ​​generated in each iteration as sequence elements; continuously executing the iterative calculation process, arranging the generated multiple chaotic state values ​​in order to form a first chaotic sequence, which is used for subsequent encryption key generation and random number extraction.

[0044] It should be noted that the chaotic parameters generated in scenarios one, two, and three are used to initialize the LSL composite chaotic sequence, and the corresponding chaotic sequence is generated through iterative calculation: (1) Scenario one generates the first chaotic sequence : Key generation and encryption process for attribute-based encryption; (2) Generation of the second chaotic sequence in scenario two : Used for Merkle tree node hash calculation and integrity verification; (3) Scenario 3 generates sequences Used for generating random salt values.

[0045] S4. Based on the bilinear group and utilizing the first chaotic sequence, generate the system master key and public parameters, and generate the corresponding user private key based on the user attribute set.

[0046] In this embodiment, step S4 specifically includes: selecting security parameters and generating bilinear groups and their generators with the same prime order, while defining a bilinear mapping; sequentially extracting three random numbers from the first chaotic sequence, which are respectively used as the first random component, the second random component, and the third random component, to jointly constitute the system master key; calculating and generating common parameters containing multiple group elements based on the system master key and the generator of the bilinear group; mapping each attribute string in the user attribute set to the corresponding group element through a hash function; sequentially extracting a master random number from the first chaotic sequence, and extracting a corresponding attribute random number for each attribute in the user attribute set; and calculating and generating the master key component and multiple attribute key components of the user private key based on the system master key, the master random number, the attribute random numbers, and the group elements after mapping each attribute.

[0047] It should be noted that the attribute-based encryption system is constructed using a bilinear group structure, through the first chaotic sequence. Enhance randomness to achieve fine-grained access control and dynamic permission management.

[0048] Specifically, the implementation process of step S4 includes: (1) Bilinear group settings Select safety parameters based on safety requirements. (Security levels, common standard values ​​are 128, 192, or 256 bits, where 128 bits are suitable for general applications, 192 bits are suitable for high-security applications, and 256 bits are suitable for the highest security level applications.) Generate a bilinear group. ,in, ( (Prime order). generator and generator (The generator satisfies that all elements in the group can be represented as powers of the generator), define a bilinear mapping. (satisfies bilinearity:) .

[0049] (2) Chaos-enhanced master key generation Traditional attribute-based encryption uses a pseudo-random number generator, while this invention uses the first chaotic sequence generated in scenario one. Enhance the randomness of the master key. Master key (MasterKey) contains three random number components. The specific extraction method is as follows: , , ,in, Represents the first chaotic sequence The It uses a single element to replace the traditional pseudo-random number generator, ensuring stronger randomness and unpredictability.

[0050] (3) Generation of common parameters Common parameters The PublicKey contains group elements derived from the master key. and attribute public key set Pairing and group parameters are configured for internal system consistency and are not listed individually as publicly accessible fields.

[0051] In this embodiment, the user key generation process includes: (1) User attribute processing By standardizing user attributes, the attributes are converted into cryptographically secure hash values, providing standardized input for subsequent key generation.

[0052] users The attribute set is defined as ,in, For the number of attributes, Indicates the first Each attribute. Calculate hash value ,in This represents a hash function (which maps attribute strings to group elements).

[0053] (2) Chaotic random number extraction High-quality random numbers are extracted from chaotic sequences to replace traditional pseudo-random number generators, providing stronger randomness and unpredictability for key generation.

[0054] The first chaotic sequence generated using scenario one Extract random numbers in sequence: main random number Extracting random numbers from chaotic sequences; Extract sequentially from the chaotic sequence.

[0055] (3) Standard ABE key generation A user's private key is generated based on user attributes and chaotic random numbers, ensuring that only users who meet the access policy can decrypt the corresponding ciphertext. First Chaotic Order It replaces traditional pseudo-random number generators, providing stronger randomness and unpredictability, and ensuring the security of key generation.

[0056] User private key (SecretKey) contains the master key component. and attribute key components Master key components ,in It is a bilinear group generator, Master key The random number component in This is the master random number extracted from the chaotic sequence. For the user attribute set... Each attribute in Generate the corresponding key components ,in It is a bilinear group generator, For attributes The hash value (maps attributes to group elements). These are random numbers representing attributes extracted from a chaotic sequence.

[0057] It should be noted that traditional attribute-based encryption systems struggle to support dynamic permission adjustments; therefore, this invention employs a first chaotic sequence. Enable dynamic updating of attributes and adjustment of permissions: Mathematical process for adjusting permissions: (1) Attribute difference calculation Calculate the set of attributes for new users and delete user attribute set ,in For the new user attribute set, This is the current user's attribute set.

[0058] Calculate changes to the user attribute set, identify attributes that need to be added and deleted, and provide accurate change information for subsequent key component updates.

[0059] (2) Add attribute key generation Generate corresponding key components for the new attributes, using the first chaotic sequence to provide high-quality randomness, ensuring the security and unpredictability of the new key components.

[0060] For each new attribute From the first chaotic sequence in scenario one Extracting the stochastic index Generate the corresponding key components .in, It is a bilinear group generator, For user private key master key components The random numbers contained For attributes The hash value (maps attributes to group elements). Attributes extracted from chaotic sequences The corresponding random number.

[0061] (3) Key component update Integrate the newly added and retained key components to generate updated user keys, ensuring that users can access data corresponding to new attributes while maintaining access permissions to existing data.

[0062] Integrate the newly added and retained key components to generate an updated user key. , containing master key components and the updated user attribute set Key component for all attributes .

[0063] This mechanism supports the dynamic addition, deletion, and modification of attributes, enabling efficient dynamic permission management.

[0064] S5. Use the second chaotic initial parameters to drive the LSL composite chaotic sequence iteration to generate the second chaotic sequence.

[0065] In this embodiment, step S5 specifically includes: receiving a second chaotic initial parameter, which includes at least a third initial state value, a fourth initial state value, and corresponding control parameters; setting the third and fourth initial state values ​​as the first two initial states of the LSL composite chaotic sequence, and inputting the corresponding control parameters into the mathematical model of the LSL composite chaotic sequence; based on the mathematical model of the LSL composite chaotic sequence, using the two initial states as input, iteratively calculating subsequent chaotic state values, and using the chaotic state values ​​generated in each iteration as sequence elements; continuously executing the iterative calculation process, arranging the generated multiple chaotic state values ​​in sequence to form a second chaotic sequence, which is used to generate dynamic salt values ​​for data blocks after the data to be transmitted is divided into blocks.

[0066] S6. Use the first chaotic sequence to provide random numbers, and perform attribute-based encryption on the data to be transmitted according to the access policy and public parameters to generate a ciphertext set.

[0067] In this embodiment, step S6 specifically includes: defining the access strategy as an access structure consisting of an access matrix and a row-to-attribute mapping function; and dividing the data to be transmitted into blocks to obtain multiple data blocks. A master random number is sequentially extracted from the first chaotic sequence, and corresponding row and column random numbers are sequentially extracted for each row of the access matrix. Based on the public parameters, the system master key, and the extracted master random number, the original message of each data block is calculated to generate the message ciphertext component corresponding to each data block. Based on the public parameters and the master random number, the master ciphertext component corresponding to each data block is generated. For each row of the access matrix, based on the attribute hash value corresponding to that row, the extracted row and column random numbers, a pair of row ciphertext components corresponding to each data block is generated. The message ciphertext component, master ciphertext component, and row ciphertext components corresponding to all rows of each data block are combined to form the complete ciphertext of that data block. The ciphertexts of all data blocks together form a ciphertext set.

[0068] In this embodiment, data encryption uses the public parameter PK and master key MK generated by the above process.

[0069] like Figure 2 As shown, the data encryption process includes: (1) Access policy definition The access policy definition adopts the standard Linear Secret Sharing Scheme (LSSS), which is the same as the access policy definition method in general attribute-based encryption schemes. Access Structure From the matrix and mapping function Composition, in which for Access matrix ( The number of rows in the matrix corresponds to the number of conditions in the access strategy. (where is the number of columns in the matrix, corresponding to the dimension of the master key). A mapping function from row to attribute (transferring the row of the matrix to the attribute) Rows are mapped to corresponding attributes ).

[0070] Define an access control policy, converting it into matrix form to provide access control rules for subsequent encryption processes. The access policy definition uses the user attribute set and system parameters established in the above process. The access policy structure in this step is consistent with the general attribute-based encryption scheme. The innovation of this invention lies in the use of chaotic sequences to generate the random numbers required for encryption in subsequent steps.

[0071] (2) Chaotic random number extraction Random numbers are extracted from chaotic sequences to provide high-quality randomness for ciphertext generation, enhancing its security and unpredictability. The first chaotic sequence generated using Scenario 1 is used. Extract random numbers in sequence: main random number Extracting random numbers from chaotic sequences; Extract sequentially from the chaotic sequence.

[0072] (3) Standard ABE ciphertext generation The ciphertext structure (including the composition and calculation formula of the ciphertext components) is consistent with the standard attribute-based encryption scheme. The innovation of this step lies in the random number generation in the standard ABE scheme. , , These random numbers are typically generated by a pseudo-random number generator, but in this application, they are extracted from chaotic sequences, thereby leveraging the good randomness and unpredictability of chaotic systems to enhance the security of the ciphertext.

[0073] Cipher (Ciphertext) contains the ciphertext component of the message. Master ciphertext component And for each row of the access matrix Generate line ciphertext components and .in, This is the original message. Bilinear pairing function , , They are bilinear groups , generator, The random number component in the master key. The master random number is extracted from the chaotic sequence. These are the row and column random numbers extracted from the chaotic sequence. For the access matrix number The attributes corresponding to the row For attributes The hash value (maps the attribute to the group element).

[0074] First chaotic sequence High-quality random numbers are provided for the encryption process, enhancing the security and unpredictability of the ciphertext. Ciphertext is generated based on the access policy and chaotic random numbers, ensuring that only users meeting the access policy can decrypt the data, while guaranteeing the security and integrity of the ciphertext. The encryption process uses the generated public parameters. and master key .

[0075] S7. Use the second chaotic sequence to derive dynamic salt values ​​for the data blocks after the data to be transmitted is divided into blocks, and construct a chaotic enhanced Merkle tree based on the dynamic salt values ​​to obtain the root hash and the verification path of each data block.

[0076] In this embodiment, step S7 specifically includes: dividing the data to be transmitted into blocks to obtain multiple data blocks; deriving a unique dynamic salt value for each data block using a deterministic salt function based on the second chaotic sequence and the content and node context of each data block; combining each data block with its corresponding dynamic salt value and inputting it into a standard hash function to calculate the enhanced hash value of each leaf node; deriving a dynamic salt value for each internal node using a deterministic salt function based on the second chaotic sequence and the context of each pair of child nodes, and combining the hash values ​​of the left and right child nodes of the internal node with the salt value and inputting it into a standard hash function, iterating from bottom to top to obtain the enhanced hash value of all internal nodes; using the hash value of the top-level node obtained from the final iterative calculation as the root hash of the chaotic enhanced Merkle tree; and recording the hash values ​​and position information of all necessary sibling nodes on the path from its own hash value to the root hash for each leaf node corresponding to the data block to form the verification path of the data block.

[0077] Specifically, when constructing a chaos-enhanced Merkle tree, the formulas for calculating the leaf node hash and the internal node hash are as follows: ; ; in, For the first One data block, For dynamic salt values ​​derived from deterministic salt functions; The hash value of the leaf node; The hash value of the internal node; , These are the hash values ​​of the left and right child nodes, respectively. The dynamic salt value of this node is derived from the deterministic salt function. Represents a standard hash function. This indicates a join operation.

[0078] It should be noted that the deterministic salt function works by generating a dynamic salt value by iterating through the LSL composite chaotic sequence and extracting numerical values ​​based on the second chaotic sequence, the data block content, and the node context.

[0079] In this embodiment, the Merkle tree construction process includes: (1) Generation of chaotic salt values Using scenario two parameters and node context, a deterministic salt function is used to generate salt values: ; in, For the first The salt value of each data block; This represents a deterministic salt value generating function; Represents the node context (including metadata such as node depth and position); This represents the Merkle tree generated in scenario two used to verify the chaotic sequence; This represents the current state of the chaotic sequence in scenario two. This represents the attribute-based encrypted chaotic sequence generated in scenario one; This indicates a bridging function (encrypting attribute-based sequences). The parameter state is passed to the Merkle tree verification sequence. This enables parameter synchronization and fusion between two chaotic systems, enhancing cross-layer security binding. By generating a unique random salt value for each data block, the collision resistance and unpredictability of the hash function are enhanced, preventing malicious attackers from compromising data integrity by constructing specific inputs.

[0080] By generating a unique random salt value for each data block, the collision resistance and unpredictability of the hash function are enhanced, preventing malicious attackers from compromising data integrity by constructing specific inputs.

[0081] (2) Enhanced hash calculation ; in, For the enhanced hash value, For the first One data block, The salt value is derived from the deterministic salt function. This represents a standard hash function (such as SHA256). This indicates a join operation.

[0082] Second chaotic sequence Each node is provided with a unique random salt value to enhance the hash function's collision resistance and unpredictability. Combining data blocks with random salt values ​​to calculate enhanced hash values ​​improves the hash function's collision resistance, ensuring that even identical data blocks will produce different hash values.

[0083] (3) Tree structure construction ① Leaf node generation For each data block Use a chaotic enhanced hash function to calculate the hash of the leaf nodes. ,in For the first One data block, The salt value is derived from the deterministic salt function.

[0084] A leaf node hash value is generated for each data block, serving as the base node of the Merkle tree and providing the foundation for data integrity protection in subsequent tree structure construction.

[0085] ② Internal node generation For each pair of child nodes, calculate the hash of the internal node: ; A Merkle tree structure is built from the bottom up, combining the hash values ​​of child nodes to generate the hash of the parent node, forming a hierarchical integrity protection structure.

[0086] in, The hash value of the internal node. , These are the hash values ​​of the left and right child nodes, respectively. The salt value of this node is derived from the deterministic salt function (the input is the node context and...). , This represents a standard hash function (such as SHA256). This indicates a join operation.

[0087] ③ Root hash calculation Calculate the hash value of the Merkle tree root node as the root hash value. This serves as an integrity summary of the entire dataset, used for subsequent integrity verification and comparison.

[0088] Second chaotic sequence A dynamic random salt value is provided for each node to ensure the uniqueness and security of the tree structure. After the Merkle tree is built, a verification path is generated for each data block. These verification paths will be used by the receiver when performing integrity verification.

[0089] S8. Calculate the policy summary of the access policy, and fuse the policy summary with the root hash and the bridging material derived from the first chaotic sequence and the second chaotic sequence to generate additional authentication data.

[0090] In this embodiment, the calculation formula for generating additional authentication data is as follows: ; in, For additional authentication data; A summary of the access policy; For the root hash of the Merkle tree, Represents a standard hash function. Indicates a connection operation; For the first chaotic sequence With the second chaotic sequence Exported bridging material.

[0091] In this embodiment, the results of data encryption and Merkle tree construction are integrated to form a complete secure data packet. The process of constructing and sending the secure data packet includes: (1) Preparation of chaotic sequences Using the three generated chaotic sequences: First chaotic sequence : Key generation and encryption process used for attribute-based encryption; Second chaotic sequence : Used for generating random salt values ​​for Merkle tree nodes; Random salt value sequence Generate a unique random salt value for each data block.

[0092] (2) Data encryption and Merkle tree construction For each data block Perform attribute-based encryption using the first chaotic sequence. Provide a random number to generate ciphertext. ,in For access strategy. The encryption process uses the generated public parameters and master key.

[0093] Using the second chaotic sequence Construct an integrity verification tree for all ciphertext blocks. The Merkle tree construction process is executed, and the root hash value of the Merkle tree is calculated. And generate a verification path for each data block.

[0094] (3) Secure data packet creation and transmission Compute access policy summary and Use chaotic sequence to derive binding materials: ; ; in, To add authentication data, This is an access policy digest (a hash of the standardized access policy expression). The root hash value of the Merkle tree. This represents a standard hash function (such as SHA256). Indicates a connection operation; For message authentication code, Represents the message authentication code function (e.g.) ), Derived function for key. A chaotic sequence generated for scenario one (used for attribute base encryption). This includes contextual information (such as dataset identifier, version number, etc.).

[0095] Reference implementation: ; in, The binding tag is for reference implementation; This is a truncation function (keeping 32 bits). For standard hash functions; For additional authentication data; A collection of ciphertext; This is a bridge function connecting two chaotic sequences; the formula is a modification of the formula... A simplified implementation example replaces the complex MAC function with a direct hash calculation, reducing implementation complexity while maintaining security. In this implementation, tags are appended as suffixes to the ciphertext (e.g., "tag").

[0096] Assemble secure data packets, including ciphertext sets, Merkle tree verification paths, policy digests, root hashes, AADs, and binding tags, to complete the encapsulation and transmission of secure data packets. Three chaotic sequences are used for encryption, hash enhancement, and salt generation, respectively, to achieve multi-layered security enhancement.

[0097] S9. Using the key material derived from the first chaotic sequence, calculate the message authentication code on the ciphertext set and the additional authentication data to obtain the cross-layer security binding label.

[0098] In this embodiment, step S9 specifically includes: taking the first chaotic sequence as the main input, processing it through a predetermined key derivation function to derive key material for authentication; combining the ciphertext set with additional authentication data to form a message to be authenticated; using the derived key material to perform a message authentication code algorithm on the message to be authenticated; and using the output of the message authentication code algorithm as the final cross-layer security binding tag.

[0099] S10. Assemble the ciphertext set, verification path, policy digest, root hash, additional authentication data, and cross-layer security binding label into a secure data packet and send it.

[0100] In this embodiment, a secure data packet reception and decryption step is also included: S11. Receive secure data packets and reconstruct the first chaotic sequence and the second chaotic sequence based on the same multi-source input data as the sender.

[0101] S12. Perform chaotic-enhanced Merkle tree integrity verification using the reconstructed second chaotic sequence and the verification path and root hash in the security data packet.

[0102] In this embodiment, the verification and decryption process after the receiver receives the data packet includes integrity verification, access policy verification, and data decryption. The receiver uses the same chaotic sequence as the sender to ensure the consistency and correctness of the verification and decryption process.

[0103] Data packet reception and preliminary verification include: performing preliminary verification on the received secure data packets, checking the validity of the timestamp and the integrity of the data packets, and ensuring that the data packets have not been tampered with and are within the valid time window.

[0104] Integrity verification includes: (1) Verification path acquisition Extract the verification path corresponding to each data block from the received security data packet. The verification path contains path information from the leaf node to the root node, including the location, hash value and node ID of the sibling node.

[0105] (2) Path verification algorithm The integrity of each data block is checked using a Merkle tree verification mechanism. The verification process includes: Using the second chaotic sequence Generate chaotic salt values ​​for leaf nodes and calculate leaf node hashes; calculate upwards along the verification path, combining hash values ​​based on sibling node positions; use the second chaotic sequence. Generate chaotic salt values ​​for internal nodes and calculate the hashes of internal nodes; compare the final calculated hash values. With root hash Consistency.

[0106] The verification process uses the same second chaotic sequence Rh as the sender to ensure the consistency of the salt value and the correctness of the verification result. If the calculated root hash is inconsistent with the root hash in the data packet, it indicates that data integrity has been compromised, and the verification fails.

[0107] (3) Batch verification optimization For data packets containing multiple data blocks, verification requests for multiple data blocks can be processed in parallel, a verification path can be generated for each data block and integrity verification can be performed, significantly improving the verification efficiency for a large number of data blocks.

[0108] Specifically, access policy verification includes: checking whether user attributes meet the access policy requirements and executing a policy matching algorithm. Access policy verification ensures that only users with appropriate attributes can access the data. The verification process uses attribute information from the generated user key.

[0109] S13. After the integrity verification is passed, the reconstructed bridging material and the first chaotic sequence are used to verify the cross-layer security binding tag.

[0110] S14. After the binding tag is verified, the encrypted set is decrypted using the user's private key held by the receiving end to recover the data to be transmitted.

[0111] In this embodiment, as Figure 3 As shown, the data decryption process includes: (1) Reconstruction of bound materials Use the same first chaotic sequence as the sender. Reconstruct the binding material required for decryption, ensuring that the decryption process uses the same random number sequence as the encryption process.

[0112] (2) Access policy verification Check whether user attributes meet the access policy requirements and determine the set of rows that meet the policy. ,in To access the row number of the matrix, Indicates from 1 to (A set of integers). Verify whether the user has access rights and determine which ciphertext components can be decrypted by matching attributes.

[0113] (3) Calculation of reconstruction coefficients Calculate reconstruction coefficients Used for linear reconstruction of the master key. Reconstruction coefficients. Satisfying the linear reconstruction condition: ,in For access matrix The Row vectors.

[0114] (4) Standard ABE decryption The message is decrypted using linear reconstruction and bilinear pairing computation, employing the user's private key and master key components. and the set of row indexes that satisfy the access policy Reconstruction coefficients in Through bilinear pairing and Calculate the decryption result. Among them, It is a bilinear pairing function. , The line component of the ciphertext.

[0115] Use the user key to ciphertext blocks Perform attribute base decryption to obtain the decrypted data block. The decryption process uses the same first chaotic sequence as the encryption process. This ensures the consistency of the random numbers and the correctness of the decryption process.

[0116] The decryption results undergo final verification to ensure the correctness and integrity of the data. Verification includes checking the format of the decrypted data, integrity checks, and a final data consistency check. Upon successful verification, the decrypted data block is returned.

[0117] The receiver uses the same chaotic sequence as the sender to ensure consistency in the decryption process and the correctness of the binding material. The entire receiving and decryption process uses the generated user key, the constructed Merkle tree structure, and the generated chaotic sequence.

[0118] To verify the performance of the LSL composite chaotic sequence proposed in this invention and to quantitatively evaluate its quality as a cryptographic random source, a series of standard experiments on the dynamic characteristics of chaotic systems were conducted. The experimental results fully demonstrate that the LSL composite chaotic sequence possesses excellent chaotic properties and can meet the requirements of high-security encryption and verification applications.

[0119] Specifically, the analysis process of the bifurcation behavior of LSL composite chaotic sequences is as follows: By drawing a bifurcation diagram of the system, the system state can be visually observed as control parameters change. Long-term behavior of change. For example... Figure 4 and Figure 5 As shown, when the control parameters As the value increases from a lower level, the system state rapidly transitions from a stable fixed point to a chaotic region via a period-doubling bifurcation path. The first obvious bifurcation phenomenon occurs when... It is approximately between 0.6 and 0.8. After that, with... As the number of values ​​increases, the set of possible system states rapidly becomes denser, indicating that the system has entered a chaotic state. It is worth noting that in... Within the above-mentioned wide parameter range, the distribution of system state points consistently exhibits dense, irregular chaotic characteristics, with periodic thin stripes observed only in a very few narrow parameter windows (such as...). Figure 4 Medium and high (A portion of the interval is shown).

[0120] Experimental results show that LSL composite chaotic sequences, under controlled parameters It can maintain stable chaotic output over a long period of time within a wide range, with a very small periodic window, which provides a solid foundation for generating long-period, highly complex pseudo-random sequences.

[0121] Specifically, Lyapunov exponent analysis of LSL composite chaotic sequences. The Lyapunov exponent is a key indicator for quantifying the sensitivity of a system to initial conditions; a positive Lyapunov exponent is a hallmark of chaotic motion. For example... Figure 6 As shown The system's maximum Lyapunov exponent is at The value nearby changed from negative to positive. For the vast majority of the parameter range, the exponent remains positive, clearly confirming the chaotic nature of the system under these parameters. Although the exponent drops sharply to negative values ​​within a few extremely narrow parameter windows (corresponding to the periodic windows observed in the bifurcation diagram), overall, as... As the Lyapunov index increases, the maximum Lyapunov index shows a slow upward trend, reaching a relatively high level. Segment (e.g.) Its average level can reach the order of 2 to 3. This trend indicates that the chaos intensity (i.e., the orbital divergence rate) of the LSL system can vary with the parameters. The increase in the degree of chaos provides the possibility to adjust the "chaos" of the system according to different security intensity requirements.

[0122] Specifically, LSL composite chaotic sequence 0-1 test The 0-1 test is a numerical method that directly determines whether a system is chaotic from time series data. Its results... A value close to 1 indicates chaos, and a value close to 0 indicates periodicity. The results of a 0-1 test on the LSL system are as follows: Figure 7 As shown It can be observed that the test metrics exist It then quickly approached and stabilized near 1, maintaining a high value (K > 0.9) for the vast majority of test parameter points. Only at a few extremely narrow parameter points... The values ​​are close to 0, and these points correspond precisely to the subtle periodic stripes observed in the bifurcation diagram. The 0-1 test results statistically strongly confirm that the LSL composite chaotic sequence exhibits significant chaotic properties over a wide parameter range, and the sequences it generates are very suitable as cryptographic random numbers.

[0123] LSL Composite Chaotic Sequence Initial Value Sensitivity Test Extreme sensitivity to initial conditions is one of the core characteristics of chaotic systems and a key requirement for pseudo-random number generators in cryptographic applications. We tested the following under the same control parameters (taking different values ​​for each parameter). Given (e.g., 1, 2, 3, 4), the two initial values ​​have only about (e.g., 1, 2, 3, 4). The trajectory evolution of minute differences in magnitude is shown in the following results. Figures 8-11 As shown in the figure. Experiments show that regardless of the value of μ, the two trajectories diverge significantly within tens of iterations (approximately 20 to 40 steps), with the difference in their corresponding state values ​​rapidly increasing to an order of magnitude (close to 1) comparable to the range of the system variables (approximately [-1, 1]). This test intuitively and quantitatively demonstrates the strong initial value sensitivity of LSL composite chaotic sequences. In cryptographic applications, this means that even extremely small differences in the seed (initial value) will result in completely different keys or random number sequences, thus ensuring high security and unpredictability in the encryption process.

[0124] Conclusion: The above series of experiments comprehensively verified the excellent dynamic characteristics of the Lucas-Sine-Logistic (LSL) LSL composite chaotic sequence designed in this invention from multiple dimensions, including bifurcation behavior, Lyapunov exponent, 0-1 test, and initial value sensitivity. Its stable and strongly chaotic behavior over a wide parameter range, as well as its high initial value sensitivity, enable it to provide a high-quality, high-security source of randomness for cryptographic operations such as key generation for attribute-based encryption and salt value derivation for Merkle tree verification, which is one of the key technical supports of this application.

[0125] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for secure data transmission, characterized in that, include: Obtain user attribute sets, access policies, data to be transmitted, and system context as multi-source input data; Based on multi-source input data, a unified chaotic parameter generation mechanism is used to determine the first chaotic initial parameter for encryption and the second chaotic initial parameter for hash enhancement. The first chaotic sequence is generated by iterating the LSL composite chaotic sequence using the first chaotic initial parameters. Based on the bilinear group and utilizing the first chaotic sequence, the system master key and public parameters are generated, and the corresponding user private key is generated based on the user attribute set. The second chaotic sequence is generated by iterating the LSL composite chaotic sequence using the second chaotic initial parameters. Random numbers are provided using the first chaotic sequence, and attribute-based encryption is performed on the data to be transmitted based on the access policy and common parameters to generate a set of ciphertexts. The second chaotic sequence is used to derive dynamic salt values ​​for the data blocks after the data to be transmitted is divided into blocks, and a chaotic enhanced Merkle tree is constructed based on the dynamic salt values ​​to obtain the root hash and the verification path of each data block. Calculate the policy digest of the access policy, and fuse the policy digest with the root hash and the bridging material derived from the first chaotic sequence and the second chaotic sequence to generate additional authentication data; Using the key material derived from the first chaotic sequence, a message authentication code is calculated on the ciphertext set and the additional authentication data to obtain a cross-layer security binding tag; The encrypted set, verification path, policy digest, root hash, additional authentication data, and cross-layer security binding label are assembled into a secure data packet and sent.

2. The data secure transmission method according to claim 1, characterized in that, The process of determining the first chaotic initial parameters for encryption and the second chaotic initial parameters for hash enhancement based on multi-source input data through a unified chaotic parameter generation mechanism specifically includes: For attribute-based encryption scenarios, the user attribute set and access policy are obtained, serialized and merged into first structured data, and the hash value of the first structured data is calculated as the first fused hash. Multiple integers are extracted from the first fusion hash and input into the bitwise operation enhanced transformation function for calculation. The calculation result is normalized and mapped to the first chaotic initial parameters. For Merkle tree verification scenarios, the data content, node information, and system context are obtained, serialized, and merged into a second structured data. The hash value of this second structured data is then calculated as the second fused hash. Multiple integers are extracted from the second fusion hash and input into the bitwise operation-enhanced transformation function for calculation. The calculation result is then normalized and mapped to the second chaotic initial parameters.

3. The data secure transmission method according to claim 2, characterized in that, The bitwise operation enhancement transformation function is specifically as follows: ； in, This indicates a bitwise enhanced transformation function. and This represents two randomly extracted 32-bit integers from the fused hash value generated by the SHA256 hash function from specific inputs in three scenarios. This represents the XOR operation. This indicates a modulo operation.

4. The data secure transmission method according to claim 1, characterized in that, The mathematical model of the LSL composite chaotic sequence is as follows: ； in, Indicates the first The chaotic state value of the step; This represents the control parameters for the Logistic mapping. and This represents the first two chaotic state values.

5. The data secure transmission method according to claim 4, characterized in that, The step of using the first chaotic initial parameters to drive the iteration of the LSL composite chaotic sequence to generate the first chaotic sequence specifically includes: Receive a first chaotic initial parameter, which includes at least a first initial state value, a second initial state value, and control parameters; The first and second initial state values ​​are set as the first two initial states of the LSL composite chaotic sequence, and the control parameters are input into the mathematical model of the LSL composite chaotic sequence. Based on the mathematical model of the LSL composite chaotic sequence, the first two initial states are used as inputs to iteratively calculate the subsequent chaotic state values, and the chaotic state values ​​generated in each iteration are used as sequence elements. The iterative calculation process is executed continuously, and the generated chaotic state values ​​are arranged in order to form the first chaotic sequence, which is used for subsequent encryption key generation and random number extraction.

6. The data secure transmission method according to claim 1, characterized in that, The process of generating a system master key and public parameters based on a bilinear group and utilizing a first chaotic sequence, and generating a corresponding user private key based on a user attribute set, specifically includes: Choose a safety parameter and generate bilinear groups and their generators with the same prime order, while defining a bilinear mapping; Three random numbers are extracted sequentially from the first chaotic sequence and used as the first random component, the second random component, and the third random component, respectively, to form the system master key. Based on the system master key and the generator of the bilinear group, calculate the common parameters that generate multiple group elements; Each attribute string in the user attribute set is mapped to a corresponding group element using a hash function; Extract a master random number sequentially from the first chaotic sequence, and extract a corresponding attribute random number for each attribute in the user attribute set; Based on the system master key, master random number, attribute random number, and group elements mapped by each attribute, calculate and generate the master key component and multiple attribute key components of the user's private key.

7. The data secure transmission method according to claim 4, characterized in that, The process of using the second chaotic initial parameters to drive the iteration of the LSL composite chaotic sequence to generate the second chaotic sequence specifically includes: Receive a second chaotic initial parameter, which includes at least a third initial state value, a fourth initial state value, and corresponding control parameters; The third and fourth initial state values ​​are set as the first two initial states of the LSL composite chaotic sequence, and the corresponding control parameters are input into the mathematical model of the LSL composite chaotic sequence. Based on the LSL composite chaotic sequence mathematical model, two initial states are used as inputs to iteratively calculate subsequent chaotic state values, and the chaotic state values ​​generated in each iteration are used as sequence elements. The iterative calculation process is executed continuously, and the generated multiple chaotic state values ​​are arranged in sequence to form a second chaotic sequence, which is used to generate dynamic salt values ​​for the data blocks after the data to be transmitted is divided into blocks.

8. The data secure transmission method according to claim 1, characterized in that, The step of using a first chaotic sequence to provide random numbers and performing attribute-based encryption on the data to be transmitted according to the access strategy and common parameters to generate a ciphertext set specifically includes: The access strategy is defined as an access structure consisting of an access matrix and a row-to-attribute mapping function; The data to be transmitted is divided into blocks to obtain multiple data blocks; Extract a master random number sequentially from the first chaotic sequence, and extract the corresponding row random number and column random number sequentially for each row of the access matrix; Based on public parameters, the system master key, and the extracted master random number, the original message of each data block is calculated to generate the message ciphertext component corresponding to each data block. Based on common parameters and master random numbers, generate master ciphertext components corresponding to each data block; For each row of the access matrix, based on the attribute hash value corresponding to that row, the extracted row random number and column random number, a pair of row ciphertext components corresponding to each data block is generated; The message ciphertext component, the main ciphertext component, and the row ciphertext components corresponding to each data block are combined to form the complete ciphertext of that data block. The ciphertexts of all data blocks together form a ciphertext set.

9. The data secure transmission method according to claim 1, characterized in that, The process of using a second chaotic sequence to derive dynamic salt values ​​for data blocks after data is divided into chunks, and constructing a chaotic-enhanced Merkle tree based on these dynamic salt values ​​to obtain the root hash and the verification path for each data block, specifically includes: The data to be transmitted is divided into blocks to obtain multiple data blocks; Based on the second chaotic sequence and the content and node context of each data block, a unique dynamic salt value is derived for each data block through a deterministic salt function; Each data block is combined with its corresponding dynamic salt value and then input into a standard hash function to calculate the enhanced hash value of each leaf node. Based on the second chaotic sequence and the context of each pair of child nodes, a dynamic salt value is derived for each internal node through a deterministic salt function. The hash values ​​of the left and right child nodes of the internal node are combined with the salt value and then input into a standard hash function. The enhanced hash values ​​of all internal nodes are obtained by iterative calculation from bottom to top. The hash value of the top-level node obtained from the final iteration is used as the root hash of the chaos-enhanced Merkle tree; The leaf node corresponding to each data block records the hash values ​​and position information of all necessary sibling nodes on the path from its own hash value to the root hash, forming the verification path of the data block.

10. The data secure transmission method according to claim 1, characterized in that, When constructing the chaos-enhanced Merkle tree, the formulas for calculating the leaf node hash and the internal node hash are as follows: ； ； in, For the first One data block, For dynamic salt values ​​derived from deterministic salt functions; , The hash value of the leaf node; The hash value of the internal node; , These are the hash values ​​of the left and right child nodes, respectively. The dynamic salt value of this node is derived from the deterministic salt function. Represents a standard hash function. This indicates a join operation.

11. The data secure transmission method according to claim 9 or 10, characterized in that, The deterministic salt function works by generating a dynamic salt value by iterating through the LSL composite chaotic sequence and extracting numerical values ​​based on the second chaotic sequence, data block content, and node context.

12. The data secure transmission method according to claim 1, characterized in that, The specific formula for generating additional authentication data is as follows: ； in, For additional authentication data; A summary of the access policy; For the root hash of the Merkle tree, Represents a standard hash function. Indicates a connection operation; For the first chaotic sequence With the second chaotic sequence Exported bridging material.

13. The data secure transmission method according to claim 1, characterized in that, The step of using key material derived from the first chaotic sequence to calculate a message authentication code on the ciphertext set and additional authentication data to obtain a cross-layer security binding tag specifically includes: Using the first chaotic sequence as the main input, it is processed through a predetermined key derivation function to derive key material for authentication. The ciphertext set is combined with additional authentication data to form the message to be authenticated; The derived key material is used to perform a message authentication code algorithm calculation on the message to be authenticated; The output of the message authentication code algorithm is used as the final cross-layer security binding label.

14. The data secure transmission method according to claim 1, characterized in that, It also includes secure data packet reception and decryption steps: Receive secure data packets and reconstruct the first and second chaotic sequences based on the same multi-source input data as the sender; Chaos-enhanced Merkle tree integrity verification is performed using the reconstructed second chaotic sequence and the verification path and root hash in the security packet; After the integrity verification is passed, the reconstructed bridging material and the first chaotic sequence are used to verify the cross-layer security binding tag. After the binding tag is successfully verified, the encrypted set is decrypted using the user's private key held by the receiving end to recover the data to be transmitted.

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