Chaotic encryption method and device based on fusion of multiple physiological characteristics and storage medium

Abstract

The present application belongs to the technical field of chaotic encryption, and particularly relates to a chaotic encryption method based on multi-element physiological feature fusion. First, initial data of multi-element physiological feature fusion is acquired. Then, a multi-dimensional chaotic random sequence generation model is constructed, a preset chaotic mapping dimension is taken as a dimension parameter of the multi-dimensional chaotic random sequence generation model, a mapping representation precision is taken as a quantization precision of the multi-dimensional chaotic random sequence generation model, the initial data is input into the multi-dimensional chaotic random sequence generation model as an initial parameter, and a chaotic random sequence is output. Finally, binary plaintext sequences are generated by processing to-be-encrypted plaintext information, and the binary plaintext sequences are subjected to bit-by-bit exclusive OR operation with the chaotic random sequence to generate encrypted data. The present application can solve the problems of poor randomness of encrypted sequences and limited encryption strength of existing single-mapping encryption algorithms.

Description

Technical Field

[0001] This invention belongs to the field of chaotic encryption technology, and particularly relates to a chaotic encryption method, device and storage medium based on the fusion of multiple physiological characteristics. Background Technology

[0002] In wireless human body local area networks, wearable sensors can acquire human physiological characteristic parameters such as electrocardiogram (ECG), electroencephalogram (EEG), gait information, and electromyography (EMG). These human physiological characteristic parameters have good individual variability, randomness, and time-varying characteristics, and are often used as important resources in security fields such as identity authentication and data encryption.

[0003] Currently, encryption using only human physiological parameters still suffers from high correlation and predictability. Therefore, existing technologies often combine it with cryptographic algorithms to improve unpredictability and non-correlation. However, the existing cryptographic algorithms used are single-mapping encryption algorithms. Single-mapping has a small key space, and the generated encryption sequence has poor randomness when used alone. Especially under low hardware precision conditions, it is prone to non-ideal periodic effects, thus limiting the encryption strength. Summary of the Invention

[0004] The technical problem solved by this invention is to provide a chaotic encryption method, device and storage medium based on the fusion of multiple physiological characteristics, so as to solve the problems of poor randomness of encryption sequence and limited encryption strength of existing single-mapping encryption algorithms.

[0005] The basic solution provided by this invention is a chaotic encryption method based on the fusion of multiple physiological characteristics, comprising:

[0006] S1: Obtain initial data for the fusion of multiple physiological characteristics;

[0007] S2: Construct a multidimensional chaotic random sequence generation model, and preset the chaotic mapping dimension and mapping representation precision. Use the preset chaotic mapping dimension as the dimension parameter of the multidimensional chaotic random sequence generation model, and the mapping representation precision as the quantization precision of the multidimensional chaotic random sequence generation model. Input the initial data into the multidimensional chaotic random sequence generation model as the initial parameter, and output a chaotic random sequence.

[0008] S3: Process the plaintext information to be encrypted to generate a binary plaintext sequence, and perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data.

[0009] Furthermore, S2 includes:

[0010] S2-1: Construct a multidimensional chaotic random sequence generation model based on Logistic mapping, Tent mapping and Cubic mapping, and regard Logistic mapping as a one-dimensional mapping, Tent mapping as a two-dimensional mapping and Cubic mapping as a three-dimensional mapping.

[0011] S2-2: The preset chaotic mapping dimension and mapping representation precision include three dimensions. The preset chaotic mapping dimension is used as the dimension parameter of the multidimensional chaotic random sequence generation model, and the mapping representation precision is used as the quantization precision of the multidimensional chaotic random sequence generation model. The three initial chaotic parameters are input into the multidimensional chaotic random sequence generation model as the initial parameters of the corresponding chaotic mapping.

[0012] S2-3: Initialize the initial parameters of the Logistic mapping, and iterate the Logistic mapping according to the quantization precision to generate a chaotic sequence X. Determine whether the length of the chaotic sequence X meets the preset length threshold. If it does not meet the threshold, execute S2-4. If it does meet the threshold, then characterize the chaotic sequence X as a chaotic random sequence.

[0013] S2-4: Determine if the dimension parameter belongs to the first dimension. If yes, return to S2-3, update the initial parameters of the Logistic mapping, iterate the Logistic mapping according to the quantization precision, generate a chaotic sequence X1, and determine if the length of the chaotic sequence X1 meets the preset length threshold. If not, update the initial parameters of the Logistic mapping again and perform the iteration operation until the preset length threshold is met, and represent the chaotic sequence X1 as a chaotic random sequence. If the dimension parameter does not belong to the first dimension, execute S2-5.

[0014] S2-5: Initialize the initial parameters of the Tent mapping, iterate the Tent mapping according to the quantization precision to generate a chaotic sequence Y, and determine whether the dimension parameter belongs to the second dimension. If it does, return to S2-3, use the iteration result of the chaotic sequence Y as the initial value for updating the Logistic mapping, and perform iteration and judgment operations. If not, execute S2-6.

[0015] S2-6: Initialize the initial parameters of the Cubic mapping, iterate the Cubic mapping according to the quantization precision to generate a chaotic sequence Z, use the chaotic sequence Z as the initial parameters of the Tent mapping in S2-5, perform iterative operations and output a chaotic random sequence.

[0016] Furthermore, the formula for calculating the Logistic mapping is:

[0017] x n+1 =μx n (1-x n ), x n∈[0,1], n=0,1…

[0018] Where μ represents the branch parameter, x n Let x represent the input value of the nth iteration, which, after being processed by the mapping function, generates the result x for the next iteration. n+1 .

[0019] Furthermore, the Tent mapping calculation formula is as follows:

[0020]

[0021] Where α represents the branch parameter of the Tent mapping, y n Let y represent the input value of the nth iteration, which, after being calculated by the mapping function, generates the result y for the next iteration. n+1 .

[0022] Furthermore, the Cubic mapping calculation formula is as follows:

[0023]

[0024] Where ρ represents the branch parameter of the Cubic mapping, z n Let z represent the input value of the nth iteration, which, after being calculated by the mapping function, generates the result z for the next iteration. n+1 .

[0025] Furthermore, S3 includes:

[0026] S3-1: Obtain the plaintext information to be encrypted and generate a binary plaintext sequence from the plaintext information;

[0027] S3-2: The random chaotic sequence is judged by a preset threshold judgment algorithm to generate a binary encrypted sequence. The preset threshold judgment algorithm is as follows:

[0028]

[0029] Among them, S n Represents a binary encrypted sequence, d n Represents a chaotic random sequence;

[0030] S3-3: Perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data.

[0031] Furthermore, the bitwise XOR operation in S3-3 specifically includes:

[0032]

[0033] Among them, Z n Indicates encrypted data, I n This represents a binary plaintext sequence.

[0034] An electronic device includes a processor and a memory, wherein the memory stores programs or instructions, and the processor executes the chaotic encryption method based on the fusion of multiple physiological characteristics as described above by calling the programs or instructions stored in the memory.

[0035] A computer-readable storage medium storing a program or instructions that causes a computer to execute the chaotic encryption method based on the fusion of multiple physiological characteristics as described above.

[0036] The principle and advantages of this invention are as follows: The technical solution of this application first acquires human physiological characteristics of a wireless human body local area network, including electroencephalogram (EEG), electrocardiogram (ECG), and gait. The acquired human physiological characteristics are then subjected to multimodal fusion and normalization processing, enabling the generated data to serve as input data for a constructed multidimensional chaotic random sequence generation model. During the operation of the multidimensional chaotic random sequence generation model, the acquired and processed human physiological characteristics are used as input data. A preset chaotic mapping dimension is used as the dimension parameter, and a preset mapping representation precision is used as the quantization precision to generate a chaotic random sequence. During the generation process, by judging the dimension parameter, an appropriate mapping function is selected for iterative calculation. Compared with existing single-mapping chaotic systems, this method has a larger key space, higher randomness, and can select appropriate dimensions and corresponding representation precision combinations according to security scenario requirements, thereby improving the security of encrypted data. Attached Figure Description

[0037] Figure 1 This is a flowchart of an embodiment of the present invention;

[0038] Figure 2 This is a flowchart illustrating the multidimensional chaotic random sequence generation model according to an embodiment of the present invention.

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0040] The following detailed description illustrates the specific implementation method:

[0041] The reference numerals in the accompanying drawings include: electronic device 400, processor 401, memory 402, input device 403, and output device 404.

[0042] The basic implementation examples are as follows: Figure 1 As shown: A chaotic encryption method based on the fusion of multiple physiological characteristics includes:

[0043] S1: Obtain initial data for the fusion of multiple physiological characteristics;

[0044] In this embodiment, the initial data uses human physiological feature parameters collected based on a wireless human body local area network structure, such as electrocardiogram (ECG), electroencephalogram (EEG), and gait information. To extract features from these physiological feature parameters, this application employs different extraction methods for different physiological features. Specifically, for ECG features, a method based on singular feature point intervals is used; for EEG features, a method based on a deep learning network architecture is used; and for gait features, a method based on a convolutional neural network is used. Finally, the extracted physiological features, including ECG, EEG, and gait features, are fused using multi-dimensional physiological feature fusion to generate the required initial data. For example, the fused result is a 3×2 vector matrix. Subsequently, the vector matrix E is split into three 1×2 sub-matrices, and each is normalized to the chaotic range of the initial parameters of the mapping function of the multidimensional chaotic random sequence generation model.

[0045] S2: Construct a multidimensional chaotic random sequence generation model, and preset the chaotic mapping dimension and mapping representation precision. Use the preset chaotic mapping dimension as the dimensional parameter of the multidimensional chaotic random sequence generation model, and the mapping representation precision as the quantization precision of the multidimensional chaotic random sequence generation model. Input the initial data into the multidimensional chaotic random sequence generation model as the initial parameters, and output a chaotic random sequence; wherein, S2 includes:

[0046] S2-1: Construct a multidimensional chaotic random sequence generation model based on Logistic mapping, Tent mapping and Cubic mapping, and regard Logistic mapping as a one-dimensional mapping, Tent mapping as a two-dimensional mapping and Cubic mapping as a three-dimensional mapping.

[0047] S2-2: The preset chaotic mapping dimension and mapping representation precision include three dimensions. The preset chaotic mapping dimension is used as the dimension parameter of the multidimensional chaotic random sequence generation model, and the mapping representation precision is used as the quantization precision of the multidimensional chaotic random sequence generation model. The three initial chaotic parameters are input into the multidimensional chaotic random sequence generation model as the initial parameters of the corresponding chaotic mapping.

[0048] S2-3: Initialize the initial parameters of the Logistic mapping, and iterate the Logistic mapping according to the quantization precision to generate a chaotic sequence X. Determine whether the length of the chaotic sequence X meets the preset length threshold. If it does not meet the threshold, execute S2-4. If it does meet the threshold, then characterize the chaotic sequence X as a chaotic random sequence.

[0049] S2-4: Determine if the dimension parameter belongs to the first dimension. If yes, return to S2-3, update the initial parameters of the Logistic mapping, iterate the Logistic mapping according to the quantization precision, generate a chaotic sequence X1, and determine if the length of the chaotic sequence X1 meets the preset length threshold. If not, update the initial parameters of the Logistic mapping again and perform the iteration operation until the preset length threshold is met, and represent the chaotic sequence X1 as a chaotic random sequence. If the dimension parameter does not belong to the first dimension, execute S2-5.

[0050] S2-5: Initialize the initial parameters of the Tent mapping, iterate the Tent mapping according to the quantization precision to generate a chaotic sequence Y, and determine whether the dimension parameter belongs to the second dimension. If it does, return to S2-3, use the iteration result of the chaotic sequence Y as the initial value for updating the Logistic mapping, and perform iteration and judgment operations. If not, execute S2-6.

[0051] S2-6: Initialize the initial parameters of the Cubic mapping, iterate the Cubic mapping according to the quantization precision to generate a chaotic sequence Z, use the chaotic sequence Z as the initial parameters of the Tent mapping in S2-5, perform iterative operations and output a chaotic random sequence.

[0052] In this embodiment, as Figure 2 As shown, the multidimensional chaotic random sequence generation model is constructed from the Logistic map, Tent map, and Cubic map. S3: Process the plaintext information to be encrypted to generate a binary plaintext sequence, and then perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data. The Logistic map, as a one-dimensional mapping in the multidimensional chaotic random sequence generation model, is expressed by the following formula:

[0053] x n+1 =μx n (1-x n ), x n ∈[0,1], n=0,1…

[0054] Where μ represents the branch parameter, x n Let x represent the input value of the nth iteration, which, after being processed by the mapping function, generates the result x for the next iteration. n+1 In this application, when the branch parameter μ∈[0,4], it can be guaranteed that x n Always located within [0,1]; when the input is a fixed μ and the initial value x0, the chaotic system will generate a corresponding random sequence x0,x1,…,x through iteration. nAs the value of μ changes, the Logistic mapping will exhibit periodic bifurcation. When 3.569 ≤ μ ≤ 4, the system enters a chaotic state, and the sequence generated by the system's iterations is a non-periodic and non-convergent pseudo-random sequence. Outside this range, the sequence after multiple iterations will converge to a specific value. As the value of μ increases, the system will exhibit different dynamic behaviors. The closer μ approaches 4, the more uniformly the values ​​generated by iterations are distributed within the interval [0,1]. When the system is in a chaotic state, the initial value x0 changes slightly, and as the number of iterations increases, the two generated sequences x... n There will also be significant differences, as the Logistic mapping is highly sensitive to the initial value x0 and the branch parameter μ;

[0055] The Tent mapping, as a two-dimensional mapping in a multidimensional chaotic random sequence generation model, is calculated using the following formula:

[0056]

[0057] Where α represents the branch parameter of the Tent mapping, and y represents the input value of the nth iteration. After calculation by the mapping function, the result y of the next iteration is generated. n+1 The branch parameter α∈(0,1) is in a chaotic state within its possible range. When α=0.5, the system exhibits a short-period state. Therefore, this application does not set α=0.5 when setting the branch parameter in the Tent mapping, and the initial value y0 of the system cannot be the same as α, so that the sequence generated by the iteration will not be periodic and the system will not evolve into a periodic system.

[0058] The Cubic mapping, as a three-dimensional mapping in a multidimensional chaotic random sequence generation model, is calculated using the following formula:

[0059]

[0060] Where ρ represents the branch parameter of the Cubic mapping, z n Let z represent the input value of the nth iteration, which, after being calculated by the mapping function, generates the result z for the next iteration. n+1 When the branch parameter ρ∈(0,2.59), it can be guaranteed that z n The Cubic mapping always lies within [0,1]. As ρ increases, it exhibits periodic bifurcation. When ρ∈(2.3,2.59), the system is chaotic, and the iterative sequence is non-periodic and non-convergent. When ρ<2.3, the sequence converges to a specific value. The closer ρ is to 2.59, the more uniformly the iterative values ​​are distributed in [0,1]. When the system is chaotic, the Cubic mapping is highly sensitive to the initial value z0 and the branch parameter ρ. Two initial values ​​with relatively small errors can yield two significantly different sequences after a long period of iteration.

[0061] Furthermore, the preset mapping representation precision in this application includes 16-bit fixed-point, 24-bit fixed-point, and 32-bit fixed-point, which are used as the quantization precision of the multidimensional chaotic random sequence generation model.

[0062] S3: Process the plaintext information to be encrypted to generate a binary plaintext sequence, and perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data; wherein, S3 includes:

[0063] S3-1: Obtain the plaintext information to be encrypted and generate a binary plaintext sequence from the plaintext information;

[0064] S3-2: The random chaotic sequence is judged by a preset threshold judgment algorithm to generate a binary encrypted sequence. The preset threshold judgment algorithm is as follows:

[0065]

[0066] Among them, S n Represents a binary encrypted sequence, d n Represents a chaotic random sequence;

[0067] S3-3: Perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data; the bitwise XOR operation is as follows:

[0068]

[0069] Among them, Z n Indicates encrypted data, I n This represents a binary plaintext sequence.

[0070] In this embodiment, plaintext information includes images, videos, and messages. This application uses images as an example. The image's pixel value is 8 bits, the grayscale value range is [0, 255], and the resolution is 512×512. The pixel values ​​of the two-dimensional grayscale image will be flattened into a one-dimensional pixel array I of n = 512×512×8. n Preparing for encryption.

[0071] Therefore, this invention uses three chaotic functions—Logistic mapping, Tent mapping, and Cubic mapping—as multidimensional chaotic random sequence generation models for a dynamic dimensionality and precision chaotic encryption algorithm. Then, based on the scenario's security requirements, it dynamically selects one-dimensional (Logistic), two-dimensional (Logistic-Tent), and three-dimensional (Logistic-Tent-Cubic) chaotic mappings and their corresponding representation precisions (16-bit fixed-point, 24-bit fixed-point, and 32-bit fixed-point) combinations to generate chaotic random sequences. Finally, it performs a bit-by-bit logical XOR operation between the original data to be encrypted and the generated chaotic random sequence, effectively ensuring the masking of plaintext information. This invention meets the system's requirements for real-time operation and high security.

[0072] In another embodiment of this example, an electronic device is also included, such as... Figure 3 As shown, the electronic device 400 includes one or more processors 401 and memory 402.

[0073] The processor 401 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0074] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 401 may execute the program instructions to implement the chaotic encryption method based on multi-physiological feature fusion of any embodiment of the present invention described above, and / or other desired functions. Various contents such as initial extrinsic parameters and thresholds may also be stored in the computer-readable storage medium.

[0075] In one example, the electronic device 400 may further include an input device 403 and an output device 404, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device 403 may include, for example, a keyboard, a mouse, etc. The output device 404 may output various information to the outside, including warning messages, braking force, etc. The output device 404 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0076] Of course, for the sake of simplicity, Figure 3Only some of the components of the electronic device 400 relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 400 may include any other suitable components depending on the specific application.

[0077] In addition to the methods and devices described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps of the chaotic encryption method based on the fusion of multiple physiological features provided in any embodiment of the present invention.

[0078] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0079] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the chaotic encryption method based on the fusion of multiple physiological features provided in any embodiment of the present invention.

[0080] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0081] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A chaotic encryption method based on the fusion of multiple physiological characteristics, characterized in that: include: S1: Obtain initial data for the fusion of multiple physiological characteristics; S2: Construct a multidimensional chaotic random sequence generation model, and preset the chaotic mapping dimension and mapping representation precision. Use the preset chaotic mapping dimension as the dimension parameter of the multidimensional chaotic random sequence generation model, and the mapping representation precision as the quantization precision of the multidimensional chaotic random sequence generation model. Input the initial data into the multidimensional chaotic random sequence generation model as the initial parameter, and output a chaotic random sequence. S3: Process the plaintext information to be encrypted to generate a binary plaintext sequence, and perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data; S2 includes: S2-1: Construct a multidimensional chaotic random sequence generation model based on Logistic mapping, Tent mapping and Cubic mapping, and regard Logistic mapping as a one-dimensional mapping, Tent mapping as a two-dimensional mapping and Cubic mapping as a three-dimensional mapping. S2-2: The preset chaotic mapping dimension and mapping representation precision include three dimensions. The preset chaotic mapping dimension is used as the dimension parameter of the multidimensional chaotic random sequence generation model, and the mapping representation precision is used as the quantization precision of the multidimensional chaotic random sequence generation model. The three initial chaotic parameters are input into the multidimensional chaotic random sequence generation model as the initial parameters of the corresponding chaotic mapping. S2-3: Initialize the initial parameters of the Logistic mapping, and iterate the Logistic mapping according to the quantization precision to generate a chaotic sequence X. Determine whether the length of the chaotic sequence X meets the preset length threshold. If it does not meet the threshold, execute S2-4. If it does meet the threshold, then characterize the chaotic sequence X as a chaotic random sequence. S2-4: Determine if the dimension parameter belongs to the first dimension. If so, return to S2-3, update the initial parameters of the Logistic mapping, and iterate the Logistic mapping according to the quantization precision to generate a chaotic sequence. And determine the chaotic sequence If the length of the sequence does not meet the preset length threshold, the initial parameters of the Logistic mapping are updated again and the iteration operation is performed until the preset length threshold is met, thus converting the chaotic sequence into a single sequence. Represent a chaotic random sequence; if the dimension parameter does not belong to the first dimension, then execute S2-5; S2-5: Initialize the initial parameters of the Tent mapping, iterate the Tent mapping according to the quantization precision to generate a chaotic sequence Y, and determine whether the dimension parameter belongs to the second dimension. If it does, return to S2-3, use the iteration result of the chaotic sequence Y as the initial value for updating the Logistic mapping, and perform iteration and judgment operations. If not, execute S2-6. S2-6: Initialize the initial parameters of the Cubic mapping, iterate the Cubic mapping according to the quantization precision to generate a chaotic sequence Z, use the chaotic sequence Z as the initial parameters of the Tent mapping in S2-5, perform iterative operations and output a chaotic random sequence.

2. The chaotic encryption method based on the fusion of multiple physiological features according to claim 1, characterized in that: The formula for calculating the Logistic mapping is: in, Indicates branch parameters, This represents the input value for the nth iteration, which, after being processed by the mapping function, generates the result for the next iteration. .

3. The chaotic encryption method based on the fusion of multiple physiological characteristics according to claim 2, characterized in that: The Tent mapping calculation formula is as follows: in, This represents the branch parameters of the Tent mapping. This represents the input value for the nth iteration, which, after being processed by the mapping function, generates the result for the next iteration. .

4. The chaotic encryption method based on the fusion of multiple physiological features according to claim 3, characterized in that: The Cubic mapping calculation formula is as follows: in, This represents the branch parameters of the Cubic mapping. This represents the input value for the nth iteration, which, after being processed by the mapping function, generates the result for the next iteration. .

5. The chaotic encryption method based on the fusion of multiple physiological characteristics according to claim 4, characterized in that: S3 includes: S3-1: Obtain the plaintext information to be encrypted and generate a binary plaintext sequence from the plaintext information; S3-2: The random chaotic sequence is judged by a preset threshold judgment algorithm to generate a binary encrypted sequence. The preset threshold judgment algorithm is as follows: in, Represents a binary encrypted sequence. Represents a chaotic random sequence; S3-3: Perform a bitwise XOR operation between the binary plaintext sequence and the chaotic random sequence to generate encrypted data.

6. The chaotic encryption method based on the fusion of multiple physiological features according to claim 5, characterized in that: The bitwise XOR operation in S3-3 is specifically as follows: in, Indicates encrypted data. This represents a binary plaintext sequence.

7. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores programs or instructions, and the processor executes the chaotic encryption method based on the fusion of multiple physiological features as described in any one of claims 1-6 by calling the programs or instructions stored in the memory.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instructions that cause a computer to execute the chaotic encryption method based on the fusion of multiple physiological characteristics as described in any one of claims 1-6.

Images