Hyperchaotic key one-time pad transmission method, device, equipment, medium and product

Abstract

The present invention discloses a method, apparatus, device, medium, and product for one-time, one-pad transmission of a hyperchaotic key. The method comprises the following steps: determining a chaotic function based on the chaotic parameters and a large prime number parameter obtained by decrypting a generated symmetric key; generating a key stream based on the generated random number sequence and the large prime number parameter; and transmitting one-time, one-pad data based on the key stream. This application scheme utilizes a symmetric key for preliminary encryption and, in conjunction with chaos theory, generates a one-time, one-pad key stream at each end. The generated key stream is used for one-time, one-pad data transmission, ensuring that the encrypted data can be decrypted while preventing a third party from obtaining the plaintext information. This method avoids security risks arising from key stream sharing and improves the security performance of key transmission.

Description

Technical Field

[0001] The present invention relates to the field of security technology, and in particular to a method, device, equipment, medium and product for transmitting a hyperchaotic key using a one-time pad. Background Art

[0002] The one-time pad is considered the most secure method for data transmission security. Shannon discovered and proved its theoretical significance, and Soviet mathematician Kotelnikov demonstrated its absolute security around the same time. To ensure data security, the one-time pad uses a new key for encryption and decryption between communicating parties each time they transmit data.

[0003] The existing one-time pad transmission scheme generates a symmetric key stream by generating true random numbers for each data encryption and decryption transmission. However, this method requires the symmetric key to be shared between the sender and receiver. This direct sharing of the symmetric key stream poses security risks. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a hyperchaotic key one-time pad transmission method, device, equipment, medium and product to avoid the security risks caused by key stream sharing and improve the security performance of key transmission.

[0005] The embodiment of the present invention provides a hyperchaotic key one-time pad transmission method, comprising:

[0006] Use the generated symmetric key to decrypt the obtained chaotic parameters and large prime number parameters;

[0007] Determining a chaotic function according to the chaotic parameters, and generating a random number sequence using the chaotic function;

[0008] Generate a key stream according to the generated random number sequence and the large prime number parameter;

[0009] One-time pad data transmission is performed according to the key stream.

[0010] Preferably, determining a chaotic function according to the chaotic parameter and using the chaotic function to generate a random number sequence includes:

[0011] Determining a one-dimensional chaotic function according to a one-dimensional branch parameter in the chaotic parameters, and determining a reduced-dimensional hyperchaotic function according to a hyperchaotic parameter in the chaotic parameters;

[0012] Using the symmetric key as the initial value of one-dimensional chaos, iterating the one-dimensional chaotic function a preset number of times to determine an output sequence;

[0013] The initial value of the hyperchaos is determined according to the output sequence, and the determined initial value is input into the dimension reduction hyperchaos function to generate the random number sequence.

[0014] As a preferred solution, the process of generating the symmetric key includes:

[0015] True random numbers generated by SRAM PUF in a trusted execution environment;

[0016] The symmetric key is generated according to the true random number.

[0017] As a preferred solution, determining the initial value of hyperchaos according to the output sequence includes:

[0018] The initial value of the hyperchaos is selected from the output sequence in a reverse order manner.

[0019] As a preferred solution, performing one-time pad data transmission according to the key stream includes:

[0020] storing the keystream in a trusted execution environment;

[0021] When the data to be encrypted is sensitive data, encrypting the data to be encrypted using the key stream in the trusted execution environment;

[0022] When the data to be encrypted is not sensitive data, the key stream is taken out from the trusted execution environment, and the data to be encrypted is encrypted using the key stream in a conventional environment.

[0023] As a preferred solution, the process of determining the hyperchaotic parameters specifically includes:

[0024] Selecting preliminary values ​​of two-dimensional parameters of two preset two-dimensional hyperchaotic functions;

[0025] Determine the dimension reduction parameter of the dimension reduction hyperchaotic function according to the distribution diagrams of the two two-dimensional hyperchaotic functions, and obtain the dimension reduction hyperchaotic function;

[0026] Identifying the distribution graph of the dimensionality-reduced hyperchaotic function according to a pre-trained artificial intelligence model;

[0027] When the recognition result is an invalid graph, adjusting the two-dimensional branch parameters in the two-dimensional parameters within a preset range, re-determining the dimension reduction hyperchaotic function, and identifying the distribution graph of the re-determined dimension reduction hyperchaotic function;

[0028] When the recognition result is a valid graph, the latest two-dimensional parameters are used as the hyperchaotic parameters.

[0029] Furthermore, the dimension reduction parameter of the dimension reduction hyperchaotic function is determined according to the distribution diagrams of the two two-dimensional hyperchaotic functions, and the dimension reduction hyperchaotic function is obtained, including:

[0030] Determine the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area of ​​the distribution diagrams of two two-dimensional hyperchaotic functions;

[0031] Constructing a functional relationship between the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the dimensionality reduction hyperchaotic function and the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the distribution graphs of the two two-dimensional hyperchaotic functions, and the dimensionality reduction parameters;

[0032] The functional relationship is solved according to the boundary constraints of the dimension-reduced hyperchaotic function to determine the dimension-reduced parameters, and the dimension-reduced hyperchaotic function is determined according to the dimension-reduced parameters.

[0033] Furthermore, determining the upper and lower boundaries of the relative clustering region and the upper and lower boundaries of the relative discrete region of the distribution graphs of the two two-dimensional hyperchaotic functions includes:

[0034] For different two-dimensional hyperchaotic functions, the minimum number of minimum value distribution points and the maximum number of maximum value distribution points in the distribution graph of the two-dimensional hyperchaotic function are counted respectively;

[0035] Determining the distribution of relatively concentrated areas and relatively discrete areas according to the size relationship between the maximum number and the minimum number;

[0036] A demarcation value that meets the preset demarcation condition is searched between the maximum value and the minimum value as the demarcation point between the relative clustering area and the relative discrete area, and the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area are determined.

[0037] Furthermore, searching for a demarcation value that meets a preset demarcation condition between the maximum value and the minimum value includes:

[0038] When the maximum number is less than the minimum number, the minimum value is increased by a preset first step length, the current judgment value is updated, the current judgment number of the current judgment value is counted, and it is determined whether the current judgment number is greater than the minimum number;

[0039] If so, take the current judgment number as the previous judgment number, increase the current judgment value by the first step length, update the current judgment value, re-count the current judgment number, and determine whether the current judgment number is greater than the previous judgment number;

[0040] If not, calculate the ratio of the difference between the previous judgment quantity and the current judgment quantity to the cutoff value of the previous judgment quantity;

[0041] When the demarcation ratio is not greater than a preset first threshold, the current judgment quantity is used as the previous judgment quantity, the current judgment value is increased by the step size, the current judgment value is updated, the current judgment quantity is recounted, and the demarcation ratio is recalculated;

[0042] When the demarcation ratio is greater than the first threshold, the current judgment value is used as the demarcation value.

[0043] As a preferred solution, searching for a demarcation value that meets a preset demarcation condition between the maximum value and the minimum value includes:

[0044] When the maximum number is greater than the minimum number, reducing the maximum value by a preset second step length, updating the current judgment value, counting the current judgment number of the current judgment value, and determining whether the current judgment number is greater than the maximum number;

[0045] If so, taking the current judgment number as the previous judgment number, reducing the current judgment value by the second step length, updating the current judgment value, re-counting the current judgment number, and determining whether the current judgment number is greater than the previous judgment number;

[0046] If not, calculate the ratio of the difference between the previous judgment quantity and the current judgment quantity to the cutoff value of the previous judgment quantity;

[0047] When the demarcation ratio is not greater than a preset second threshold, the current judgment quantity is used as the previous judgment quantity, the current judgment value is reduced by the step size, the current judgment value is updated, the current judgment quantity is recounted, and the demarcation ratio is recalculated;

[0048] When the demarcation ratio is greater than the second threshold, the current judgment value is used as the demarcation value.

[0049] An embodiment of the present invention further provides a hyperchaotic key one-time pad transmission device, the device comprising:

[0050] A symmetric module is used to decrypt the obtained chaotic parameters and large prime number parameters using the generated symmetric key;

[0051] A chaos module, configured to determine a chaos function according to the chaos parameters, and generate a random number sequence using the chaos function;

[0052] A key generation module, configured to generate a key stream based on the generated random number sequence and the large prime number parameter;

[0053] The transmission module is used to perform one-time pad data transmission according to the key stream.

[0054] Preferably, the chaos module is specifically used for:

[0055] Determining a one-dimensional chaotic function according to a one-dimensional branch parameter in the chaotic parameters, and determining a reduced-dimensional hyperchaotic function according to a hyperchaotic parameter in the chaotic parameters;

[0056] Using the symmetric key as the initial value of one-dimensional chaos, iterating the one-dimensional chaotic function a preset number of times to determine an output sequence;

[0057] The initial value of the hyperchaos is determined according to the output sequence, and the determined initial value is input into the dimension reduction hyperchaos function to generate the random number sequence.

[0058] Preferably, the process of generating the symmetric key by the symmetric module includes:

[0059] True random numbers generated by SRAM PUF in a trusted execution environment;

[0060] The symmetric key is generated according to the true random number.

[0061] Preferably, the chaos module is specifically used for:

[0062] The initial value of the hyperchaos is selected from the output sequence in a reverse order manner.

[0063] Preferably, the transmission module is specifically used for:

[0064] storing the keystream in a trusted execution environment;

[0065] When the data to be encrypted is sensitive data, encrypting the data to be encrypted using the key stream in the trusted execution environment;

[0066] When the data to be encrypted is not sensitive data, the key stream is taken out from the trusted execution environment, and the data to be encrypted is encrypted using the key stream in a conventional environment.

[0067] Preferably, the hyperchaotic parameter determination process specifically includes:

[0068] Selecting preliminary values ​​of two-dimensional parameters of two preset two-dimensional hyperchaotic functions;

[0069] Determine the dimension reduction parameter of the dimension reduction hyperchaotic function according to the distribution diagrams of the two two-dimensional hyperchaotic functions, and obtain the dimension reduction hyperchaotic function;

[0070] Identifying the distribution graph of the dimensionality-reduced hyperchaotic function according to a pre-trained artificial intelligence model;

[0071] When the recognition result is an invalid graph, adjusting the two-dimensional branch parameters in the two-dimensional parameters within a preset range, re-determining the dimension reduction hyperchaotic function, and identifying the distribution graph of the re-determined dimension reduction hyperchaotic function;

[0072] When the recognition result is a valid graph, the latest two-dimensional parameters are used as the hyperchaotic parameters.

[0073] Furthermore, the dimension reduction parameter of the dimension reduction hyperchaotic function is determined according to the distribution diagrams of the two two-dimensional hyperchaotic functions, and the dimension reduction hyperchaotic function is obtained, including:

[0074] Determine the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area of ​​the distribution diagrams of two two-dimensional hyperchaotic functions;

[0075] Constructing a functional relationship between the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the dimensionality reduction hyperchaotic function and the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the distribution graphs of the two two-dimensional hyperchaotic functions, and the dimensionality reduction parameters;

[0076] The functional relationship is solved according to the boundary constraints of the dimension-reduced hyperchaotic function to determine the dimension-reduced parameters, and the dimension-reduced hyperchaotic function is determined according to the dimension-reduced parameters.

[0077] Furthermore, the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the distribution diagrams of the two two-dimensional hyperchaotic functions are determined, including

[0078] For different two-dimensional hyperchaotic functions, the minimum number of minimum value distribution points and the maximum number of maximum value distribution points in the distribution graph of the two-dimensional hyperchaotic function are counted respectively;

[0079] Determining the distribution of relatively concentrated areas and relatively discrete areas according to the size relationship between the maximum number and the minimum number;

[0080] A demarcation value that meets the preset demarcation condition is searched between the maximum value and the minimum value as the demarcation point between the relative clustering area and the relative discrete area, and the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area are determined.

[0081] Furthermore, searching for a demarcation value that meets a preset demarcation condition between the maximum value and the minimum value includes:

[0082] When the maximum number is less than the minimum number, the minimum value is increased by a preset first step length, the current judgment value is updated, the current judgment number of the current judgment value is counted, and it is determined whether the current judgment number is greater than the minimum number;

[0083] If so, take the current judgment number as the previous judgment number, increase the current judgment value by the first step length, update the current judgment value, re-count the current judgment number, and determine whether the current judgment number is greater than the previous judgment number;

[0084] If not, calculate the ratio of the difference between the previous judgment quantity and the current judgment quantity to the cutoff value of the previous judgment quantity;

[0085] When the demarcation ratio is not greater than a preset first threshold, the current judgment quantity is used as the previous judgment quantity, the current judgment value is increased by the step size, the current judgment value is updated, the current judgment quantity is recounted, and the demarcation ratio is recalculated;

[0086] When the demarcation ratio is greater than the first threshold, the current judgment value is used as the demarcation value.

[0087] As a preferred solution, searching for a demarcation value that meets a preset demarcation condition between the maximum value and the minimum value includes:

[0088] When the maximum number is greater than the minimum number, reducing the maximum value by a preset second step length, updating the current judgment value, counting the current judgment number of the current judgment value, and determining whether the current judgment number is greater than the maximum number;

[0089] If so, taking the current judgment number as the previous judgment number, reducing the current judgment value by the second step length, updating the current judgment value, re-counting the current judgment number, and determining whether the current judgment number is greater than the previous judgment number;

[0090] If not, calculate the ratio of the difference between the previous judgment quantity and the current judgment quantity to the cutoff value of the previous judgment quantity;

[0091] When the demarcation ratio is not greater than a preset second threshold, the current judgment quantity is used as the previous judgment quantity, the current judgment value is reduced by the step size, the current judgment value is updated, the current judgment quantity is recounted, and the demarcation ratio is recalculated;

[0092] When the demarcation ratio is greater than the second threshold, the current judgment value is used as the demarcation value.

[0093] An embodiment of the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a hyperchaotic key one-time pad transmission method as described in any of the above embodiments.

[0094] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a hyper-chaotic key one-time pad transmission method as described in any of the above embodiments.

[0095] An embodiment of the present invention further provides a computer program product, comprising a computer program / instruction, which implements the steps of the method described in any one of the above embodiments when executed by a processor.

[0096] Compared with the prior art, the present invention provides a method, apparatus, device, medium, and product for one-time, one-pad transmission of a hyperchaotic key. The method comprises the following steps: determining a chaotic function based on the chaotic parameters and generating a random number sequence using the chaotic function; generating a key stream based on the generated random number sequence and the large prime number parameters; and transmitting one-time, one-pad data based on the key stream. This application scheme utilizes a symmetric key for preliminary encryption and, in conjunction with chaos theory, generates a one-time, one-pad key stream at each end. The generated key stream is used for one-time, one-pad data transmission, ensuring that the encrypted data can be decrypted while preventing third parties from obtaining the plaintext information. This avoids the security risks associated with key stream sharing and improves the security of key transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 1 is a flow chart of a one-time-one-pad transmission method for a hyperchaotic key provided by an embodiment of the present invention;

[0098] Figure 2 is a bifurcation diagram corresponding to the branch parameters provided by an embodiment of the present invention;

[0099] Figure 3 is a schematic structural diagram of a transmission system provided by an embodiment of the present invention;

[0100] Figure 4 This is a schematic diagram of the data encryption and decryption transmission process provided by an embodiment of the present invention;

[0101] Figure 5 1 is a flow chart of a data packet encryption process provided by an embodiment of the present invention;

[0102] Figure 6 The two-dimensional hyperchaotic function X provided by the embodiment of the present invention is n Distribution map of

[0103] Figure 7 The two-dimensional hyperchaotic function Y provided by the embodiment of the present invention is n Distribution map of

[0104] Figure 8 Schematic diagram of the structure of the artificial intelligence model provided by an embodiment of the present invention;

[0105] Figure 9 This is a distribution diagram of the dimensionality reduction hyperchaotic function provided by an embodiment of the present invention;

[0106] Figure 10This is a distribution diagram of a one-dimensional logistic function when u=3.9 and the initial value is 0.4, provided by an embodiment of the present invention;

[0107] Figure 11 This is a structural diagram of a hyperchaotic key one-time pad transmission device provided by an embodiment of the present invention;

[0108] Figure 12 It is a structural diagram of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0109] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0110] It can be understood that the various embodiments described below can be combined or combined when it is logical, and the embodiments of the present invention will not describe various combinations one by one.

[0111] During data transmission, the content must be overwritten to prevent third-party access. Secure data transmission based on a key system is a common method for data transmission security. Plaintext to be transmitted is encrypted using a key. The encrypted ciphertext cannot be retrieved without the decryption key. Keys are categorized into symmetric and asymmetric keys based on how they are generated and used. Symmetric keys use the same key for both encryption and decryption, while asymmetric keys generate a pair of keys, called a public key and a private key, upon generation. The public key is used for encryption, and the private key is used for decryption.

[0112] The most commonly used data transmission confidentiality scheme currently uses an asymmetric key mechanism to secretly share the generated symmetric key. This symmetric key is always used during data transmission. This is different from a one-time pad, which updates the key once every data packet transmission. Therefore, using the same key for data transmission still poses security risks.

[0113] In order to ensure the security of data transmission, a new key will be used to encrypt and decrypt data each time the two communicating parties transmit data.

[0114] The existing one-time pad transmission scheme generates a symmetric key stream by generating true random numbers for each data encryption and decryption transmission. However, this method requires the symmetric key to be shared between the sender and receiver. This direct sharing of the symmetric key stream poses security risks.

[0115] In order to solve the above technical problems, this application provides a hyperchaotic key one-time pad transmission method, see Figure 1 , is a flow chart of a method for transmitting a hyperchaotic key using a one-time pad according to an embodiment of the present invention, the method comprising the following steps:

[0116] Step S1, using the generated symmetric key to decrypt the obtained chaotic parameters and large prime number parameters;

[0117] Step S2, determining a chaotic function according to the chaotic parameters, and using the chaotic function to generate a random number sequence;

[0118] Step S3, generating a key stream according to the generated random number sequence and the large prime number parameter;

[0119] Step S4: Perform one-time pad data transmission according to the key stream.

[0120] When this embodiment is implemented, the present invention is applied to the communication end. During the specific communication, the chaotic parameters and the large prime number parameters are first determined by generating a symmetric key.

[0121] Generate the symmetric key keyg based on the symmetric key generation principle. The symmetric key keyg is primarily used to protect data transmission security during the initial handshake between communicating parties. Specifically, the chaotic parameters and large prime number parameters obtained during the handshake are encrypted using a symmetric key. Therefore, the symmetric key is required to decrypt these parameters and obtain the chaotic parameters and large prime number parameters.

[0122] Large prime number parameters are used to generate key streams. According to the mathematical principle of discrete logarithms, under the condition of a large prime number, the following process is difficult to crack. The two ends of the data transmission are called Client1 and Client2 respectively.

[0123] First, large prime numbers p and G are shared between Client 1 and Client 2. They do not need to be encrypted and can be made public in plain text.

[0124] Client1 generates a random number a and calculates A=G a (mod p), share A with Client 2 and send it in plain text.

[0125] Client2 generates a random number b and calculates B=G b (mod p), share B with Client 1 and send it in plain text.

[0126] For the key K on Client1 a =B a (mod p)=(G b (mod p))a (mod p)=(G ba (mod p))(modp).

[0127] For the key K on Client2 b =A b (mod p)=(G a (mod p)) b (mod p)=(G ab (mod p))(modp).

[0128] Therefore, when both ends share the random number, the key K generated by both ends a =K b .

[0129] In the above process, mathematical principles ensure the anonymity of the symmetric key while enabling key sharing between both ends of the data transmission. However, this requires multiple parameter sharing between both parties throughout the process, which affects the efficiency of data transmission. This method is undoubtedly preferred in situations where a one-time pad is not required, but it is not applicable in situations where a one-time pad is required. However, it does provide relatively high security for the start of the entire task.

[0130] Existing symmetric key generation mechanisms often fail to meet the efficiency requirements of a one-time pad when generating large numbers of symmetric keys. This demonstrates that, under the existing system, generating symmetric keys requires constant interaction between large prime numbers G and P, as well as related intermediate processes, resulting in a significant waste of transmission resources. If asymmetric key transmission is employed, the asymmetric key must be transmitted before each data packet is transmitted, which is clearly unreasonable. Keystream generation often involves repeated salting of the same key. While this salting ensures key security, generating a large number of keys consumes significant storage resources.

[0131] In this case, based on initial encryption using symmetric keys, chaos theory is employed to generate a one-time key stream at each end. The random numbers generated by the chaotic function are controllable and fast, eliminating the need for key salting and iteration. The key length is also controllable, saving storage space. While ensuring one-time encryption between the communicating ends, there's no need for real-time exchange of symmetric keys, ensuring efficient and secure communication.

[0132] Most classical dynamic equations are established based on linear equations. One characteristic of linear equations is countable additivity, that is, there is a strong pattern in the changes of adjacent numbers, and the causal relationship between the variables and the dependent variable of the entire equation can often be obtained through linear equations. The dynamic equations established based on non-linear equations have strong discreteness and are particularly sensitive to initial values. In 1963, Lorenz discovered chaotic motion on a strange attractor when studying the atmospheric convection model, which means that the solutions of the atmospheric convection equation will be attracted to a relatively strange region. In this region, the distribution of the solutions of the equation is completely random according to different initial values and has no possibility of being predicted. After sufficient iterations, the positions of the solutions of the equation are completely randomly distributed within a specific region.

[0133] Chaotic functions have various forms. The system dimension is divided by the number of independent variables, and a one-dimensional logistic function and a two-dimensional hyperchaotic function are used for illustration.

[0134] The form of the one-dimensional logistic function is: x n+1 = x n u(1 - x n ), where 0 ≤ u ≤ 4 and 0 < x n ≤ 1.

[0135] As the bifurcation parameter, the bifurcation of the result distribution controlled by it is shown in Figure 2 , which is the bifurcation diagram corresponding to the bifurcation parameter provided by the embodiment of the present invention. Among them, the abscissa is the bifurcation parameter u, and the ordinate is X n .

[0136] When 3.5699456 < u ≤ 4, it enters the chaotic state. When the u value is the same, different initial values are iterated multiple times.

[0137] It can be seen from the distribution diagram of its solutions that when the equation enters the chaotic state, as the number of iterations increases, the positions of the solutions of the equation become more and more random. After iteration, the positions of the solutions of initial values that are very close to each other become more and more divergent, and it completely does not have the property of countable additivity of linear equations.

[0138] It can be seen that chaotic parameters such as the initial value and bifurcation parameter of the chaotic function have a great impact on the solutions of the chaotic function. Without knowing the initial value and bifurcation parameter, for chaotic functions with other parameters, due to their randomness, the solutions of the chaotic function cannot be determined.

[0139] Therefore, in this case, after obtaining the chaotic parameters, the chaotic function can be correspondingly determined, and a random sequence can be generated based on the chaotic function. Since the chaotic parameters are obtained through symmetric key interaction, the chaotic functions obtained by both communication parties are the same.

[0140] A key stream is generated according to the generated random number sequence and the large prime number parameter. The random number sequence that can be generated based on the same initial value serves as the same key stream.

[0141] The generated key stream is then used to transmit secret data once, ensuring that the encrypted data can be decrypted while preventing a third party from obtaining the plaintext information, thus solving the problem that the sender's data can be both decrypted and undecrypted.

[0142] In another embodiment provided by the present invention, the chaotic function used in this case includes a one-dimensional chaotic function and a hyperchaotic parameter. The process of determining the chaotic function to generate a random number sequence in step S2 specifically includes:

[0143] Determining a one-dimensional chaotic function according to a one-dimensional branch parameter in the chaotic parameters, and determining a reduced-dimensional hyperchaotic function according to a hyperchaotic parameter in the chaotic parameters;

[0144] The one-dimensional chaotic function is in the form of: n+1 =x n u(1-x n ), where 0≤u≤4, 0 <x n ≤1.

[0145] The one-dimensional chaotic function can be uniquely determined according to the one-dimensional branch parameter u in the chaotic parameters.

[0146] For hyperchaotic functions, whether an equation possesses chaotic properties can be identified by its Lyapunov exponents. The number of Lyapunov exponents for an equation is equal to the number of dimensions of the equation: a one-dimensional equation has one Lyapunov exponent, and a two-dimensional equation has two. Hyperchaos is defined when the number of Lyapunov exponents is greater than or equal to 2, and all Lyapunov exponents are greater than zero.

[0147] The two-dimensional hyperchaotic function can be expressed as follows: n+1 =f1(x n ,y n ),y n+1 =f2(x n ,y n ).

[0148] Among them, f1(x n ,y n )=a1+a2x n +a3x n 2 +a4y n +a5y n 2 +a6x n y n .

[0149] f2(x n ,y n )=b1+b2x n +b3x n 2 +b4y n +b5y n 2 +b6x n y n .

[0150] a1...a6, b1...b6 are the parameters to be found.

[0151] Since the mixed terms and quadratic terms of the equation will affect the calculation speed, the above equation can be simplified while retaining the hyperchaotic characteristics of the equation as follows: f1(x n ,y n )=a4y n +a5yn2、f2(x n ,y n )=b2x n +b4y n .

[0152] Two-dimensional data still needs to be reduced in dimension when generating random numbers. To ensure the distribution of data after dimensionality reduction, the following formula can be used for dimensionality reduction. Assume that the hyperchaotic function Fn of the data after dimensionality reduction is:

[0153] F n =m1X n +m2Y n +m3.

[0154] A dimension-reduced hyperchaotic function can be determined according to the hyperchaotic parameters m1, m2 and m3 in the chaotic parameters.

[0155] The generated symmetric key is used as the initial value of the one-dimensional chaotic function, and it is iterated for more than 20 times. The number of iterations is determined according to the actual number of tasks. According to the principle of the above chaotic function, when the iteration exceeds 4 or 5 times, the results will be differentiated. In order to ensure the randomness of the results, the number of iterations of the one-dimensional chaotic function and hyperchaos is set to a minimum of 20 times. Suppose the output sequence of the one-dimensional chaotic function is [o1, o2, ..., o n ].

[0156] When the two communicating parties are communicating, n one-dimensional chaotic sequences are generated according to the number of tasks n to be sent, and the initial value of the hyperchaos is determined.

[0157] The hyperchaotic function generates n+1 random number sequences [t1, t2, ..., t n+1 ], according to K a =K b=G1 ti mod p, i = 1, ..., n + 1, generates the key stream key1, key2, ..., key n+1 .

[0158] The u value of the one-dimensional chaotic sequence is shared in advance through the symmetric key keyg. Since the chaotic parameters of both parties A and B are the same, the sequences generated by the same initial values ​​are also the same, and therefore the key streams of both parties A and B are also the same.

[0159] It should be noted that the chaotic parameters proposed in this embodiment adopt a random number generation method combining a one-dimensional chaotic function and a hyperchaotic function. In other embodiments, a method of generating random numbers by determining a chaotic function based on chaotic parameters may be adopted, for example, a one-dimensional chaotic function or a hyperchaotic function may be used alone.

[0160] In the existing random data generation method based on chaotic functions, the generated key is still the original mechanism key generation method, and the generated random number is just a cheap replacement for the original random number, and the distribution performance of the result of the one-dimensional chaotic function is still relatively poor compared to the distribution result of the two-dimensional hyperchaotic function. The parameter update of the existing chaotic function is still relatively traditional, relying on manual or random number selection within a certain range. The result is that when unsupervised parameter update is required, it is unknown whether the distribution characteristics of the updated result are more in line with the requirements. Therefore, this case uses a one-dimensional chaotic function and a hyperchaotic function, and uses a one-dimensional logistic chaotic function + two-dimensional hyperchaotic function mechanism to generate random numbers. Therefore, a one-dimensional logistic chaotic function + two-dimensional hyperchaotic function will be used to generate random numbers in the one-time one-key process, forming a symmetric key stream unilaterally, solving the duality problem of data transmission and improving the efficiency of key generation.

[0161] In another embodiment provided by the present invention, see Figure 3 , is a structural diagram of a transmission system provided by an embodiment of the present invention.

[0162] The solution provided in this application is based on a hyperchaotic key one-time pad transmission method in a trusted execution environment for data one-time pad data transmission.

[0163] The Trusted Execution Environment (TEE) relies heavily on hardware, ensuring a high level of security. Within a server or other terminal device, a separate, independent runtime environment based on memory and CPU is created to protect the applications and data required for operation and storage. The TEE secures the runtime environment by establishing a root of trust within the hardware. This root of trust is directly tied to the unique physical characteristics of the device, enhancing the security of the TEE.

[0164] In key generation, random numbers determine the security of the key, and a good random number offers higher security. Key generation using random numbers exploits the unpredictable nature of random numbers. Based on statistical characteristics, ideal random numbers are relatively uniformly distributed, and the specific value of a random number cannot be determined using statistical methods. Real-life noise and the variability of instantaneous voltage values ​​exhibit these characteristics. Random numbers generated using physical methods like noise and instantaneous voltage are called true random numbers, while those generated using mathematical and computer principles are called pseudorandom numbers. These methods include linear congruential methods, multiple recursive methods, the Fibonacci method, the shift register method, and the reverse congruential method. Because true random numbers are generated based on actual physical properties, their generation efficiency is lower, but their quality is higher. Pseudorandom numbers, generated based on mathematical algorithms and computer principles, are more efficient but have lower quality. Among true random number generation schemes, those that generate random numbers based on the physical properties of hardware are called PUFs, which stand for Physically Unclonable Functions. The function in PUF does not refer to the concept of mathematical function. It refers to the unique physical characteristics of an object that cannot be cloned. Currently, there are two types of PUF-based random numbers: SRAM PUF and FLASH PUF.

[0165] In the trusted execution environment of the server, the symmetric key generated by the random number generated by the PUF is used as the initial value of the one-dimensional chaotic function.

[0166] Because true random numbers are highly secure and cannot be directly accessed externally within a trusted execution environment, the resulting symmetric key is also highly secure and remains within the trusted execution environment. Using the symmetric key as the initial value for a one-dimensional chaotic equation generates a data sequence that is difficult to crack. From an external perspective, these data sequences appear random. Using these random values ​​as the initial values ​​for a hyperchaotic function further ensures their randomness.

[0167] An SRAM PUF is used to generate true random numbers, and a symmetric key, keyg, is generated based on the principles of symmetric key generation. This keyg is primarily used to protect data transmission security during the initial handshake between communicating parties. Once the symmetric key is generated, the parameters associated with the hyperchaotic and one-dimensional chaotic functions, as well as the new large prime number G1 used to generate the keystream, are securely shared between the communicating parties using the symmetric key. All parameter encryption and decryption operations are performed within the trusted execution environment of both parties to ensure parameter security.

[0168] In another embodiment provided by the present invention, when determining the initial value of the dimension reduction hyperchaotic function, the initial value of the hyperchaotic function is selected from the output sequence by taking values ​​in reverse order.

[0169] That is, when the two parties formally communicate data for the first time, the last one-dimensional chaotic sequence is selected as the initial value of hyperchaos.

[0170] In another embodiment provided by the present invention, when a key stream is used for one-time data transmission, since the present case is executed in a trusted execution environment, the trusted execution environment needs to be considered when using the key, see Figure 4 , is a schematic diagram of the data encryption and decryption transmission process provided by an embodiment of the present invention.

[0171] When the communicating parties A and B perform one-time secret transmission, after obtaining the key stream, the key stream is stored in the trusted execution environment TEE.

[0172] When the encrypted data is sensitive data, data encryption and decryption are performed in a trusted execution environment, the key stream is used to encrypt the data to be encrypted in the trusted execution environment, and the data is transmitted through a data network after encryption is completed.

[0173] When the data is non-sensitive regular data, encryption and decryption operations can be performed in a regular environment. The key stream needs to be taken out of the trusted execution environment, and the key stream is used to encrypt the data to be encrypted in the regular environment.

[0174] The encryption and decryption algorithms can be processed according to the existing encryption and decryption algorithms, see Figure 5 , is a flow chart of the data packet encryption process provided by an embodiment of the present invention.

[0175] The key stream is stored in numbered order. After encryption is complete, the encrypted transmission data packet is appended with a plaintext label to identify the packet number sent, corresponding to the corresponding key stream. A flag indicating whether the chaotic parameters need to be changed is also included in the plaintext of the data packet to be encrypted. When this bit is 1, it indicates that the chaotic parameters will be changed, and the parameters to be changed and the initial value of the one-dimensional chaos function are attached. When it is 0, it indicates that the chaotic function data will not be updated. When the other party receives and decrypts the data and finds that the chaotic parameters need to be updated, it can update the chaotic parameters and re-update the key.

[0176] In another embodiment provided by the present invention, when determining the hyperchaotic parameters, the following steps are specifically performed:

[0177] The two-dimensional hyperchaotic function can be expressed as follows: n+1 =f1(x n ,y n ),y n+1 =f2(x n ,y n ).

[0178] Among them, f1(xn ,y n )=a1+a2x n +a3x n 2 +a4y n +a5y n 2 +a6x n y n .

[0179] f2(x n ,y n )=b1+b2x n +b3x n 2 +b4y n +b5y n 2 +b6x n y n .

[0180] Since the mixed terms and quadratic terms of the equation will affect the calculation speed, the above equation can be simplified while retaining the hyperchaotic characteristics of the equation, as follows:

[0181] f1(x n ,y n )=a4y n +a5y n 2 ;

[0182] f2(x n ,y n )=b2x n +b4y n ;

[0183] By selecting the initial values ​​for the two-dimensional parameters of the two-dimensional hyperchaotic functions, a4 = 1.55, a5 = -1.3, b2 = -1.1, and b4 = 0.1, the two Lyapunov exponents of the equation are now 0.238 and 0.166, respectively. All Lyapunov exponents are greater than zero, indicating that the system possesses chaotic characteristics. Using a4 as the variable, the bifurcation diagram shows that when 1.55 ≤ a4 <= 1.6, the equation enters a chaotic state. Therefore, when generating random numbers, the equation parameters can be updated by adjusting the value of a4, thereby improving the security of the random number generation equation.

[0184] According to the principle of chaotic function, when the value of a4 is changed, the distribution of the entire chaotic result will also change. By changing a4 and then changing the distribution properties of the entire hyperchaotic function, the safety level can be improved.

[0185] Let the state quantity X n and Y nThe generated iterative sequences are [X1, X2,..., X n , [Y1, Y2,..., Y n , see Figure 6 , which is the distribution diagram of the two-dimensional hyperchaotic function X n provided by the embodiment of the present invention. See Figure 7 , which is the distribution diagram of the two-dimensional hyperchaotic function Y n provided by the embodiment of the present invention.

[0186] ]The distributions of X n and Y n can be obtained through the above process. Then, according to the dimensionality reduction formula described in the chaotic function part, the dimensionality-reduced distribution F n can be obtained.

[0187] Regarding whether the generated F n meets the distribution requirements, an artificial intelligence model is used to identify the distribution diagram of F[[ID=XXX]] n . After the identification passes, the update of the hyperfunction parameters can be completed. The artificial intelligence model is trained using the Alexnet architecture.

[0188] When training the artificial intelligence model, the dataset regards the valid diagrams and invalid diagrams of the F n distribution and the one-dimensional chaotic distribution as invalid diagrams as materials. Within the range of 1.55 ≤ a4 ≤ 1.6, multiple distribution diagrams of F n are generated. The valid diagrams are labeled as 01, the invalid diagrams are labeled as 00. Under different initial values of the one-dimensional chaotic function 3.5699456 < u ≤ 4, distribution diagrams are generated and labeled as 00. The artificial intelligence model is trained to obtain the ability to distinguish whether the F n distribution is valid.

[0189] After obtaining the distribution characteristics of the dimensionality-reduced hyperchaotic function F n , the trained artificial intelligence model is used to identify whether the distribution characteristics of F n meet the requirements of the random number distribution. If it is satisfied, the relevant parameters of the existing settings are retained. If it is not satisfied, the program adjusts the two-dimensional branch parameters in the two-dimensional parameters, that is, adjusts a4 for fine-tuning until the distribution of F n meets the requirements. When the recognition result is a valid diagram, the latest two-dimensional parameters are used as the hyperchaotic parameters, and after fixing the parameters, they are sent into the trusted execution environment.

[0190] In the trusted execution environment, a hyperchaotic function and a dimensionality reduction equation are generated through the obtained determined hyperchaotic parameters, and these are shared with both communication parties using the symmetric key key through the parameters.

[0191] It should be noted that, see Figure 8, is a structural diagram of the artificial intelligence model provided by an embodiment of the present invention. The entire model has 8 layers of convolutional neural network, the first 5 layers are convolutional layers, and the last three layers are fully connected layers.

[0192] The original distribution image is processed into a 227*227 size image, and after random processing it becomes 224*224.

[0193] The convolution input of convolution layer C1 is 227*227*3, the convolution kernel size is 11*11*3, the number of convolution kernels is 96, the edge is not expanded, that is, padding = 0, stride = 4, and the Feature Map size is 55*55*96. The activation function is ReLU; the pooling kernel size of the pooling layer is 3*3, the edge is not expanded, that is, padding = 0, stride = 2, and the output of Feature Map C1 is 27*27*96.

[0194] The convolution input of the convolution layer C2 is 27*27*96, the convolution kernel size is 5*5*96, the number is 256, the expansion edge padding = 2, the stride = 1, and the FeatureMap size is 27*27*256; the activation function is Relu; the pooling kernel size of the pooling layer is 3*3, the edge is not expanded, that is, padding = 0, the stride = 2, and the output of FeatureMap C2 is 13*13*256;

[0195] The convolution input of convolution layer C3 is 13*13*256, the convolution kernel size is 3*3*256, the number is 384, the padding is 1, the stride is 1, the FeatureMap size is 13*13*384; the activation function is ReLU; there is no pooling layer, that is, the output of C3 is 13*13*384;

[0196] The convolution input of convolution layer C4 is 13*13*384, the convolution kernel size is 3*3*384, the number is 384, the expansion edge is padding=1, the step length is stride=1, the FeatureMap size is 13*13*384; the activation function is ReLU; there is no pooling layer, that is, the output of C4 is 13*13*384;

[0197] The convolution input of the convolution layer C5 is 13*13*384, the convolution kernel size is 3*3*384, the number is 256, the expansion edge padding = 1, the step length stride = 1, and the FeatureMap size is 13*13*256; the activation function is Relu; the pooling kernel size of the pooling layer is 3X3, the edge is not expanded, that is, padding = 0, the step length stride = 2, and the output of the FeatureMap, that is, C1, is 6*6*256;

[0198] The fully connected layer FC1 is implemented by convolution, with an input of 6*6*256, a convolution kernel size of 6*6*256, a number of 4096, no edge expansion padding = 0, stride = 1, and a FeatureMap size of 1*1*4096; the activation function is ReLU; some neural nodes are removed in the dropout process to prevent overfitting, and the output of FC1 is 1*1*4096;

[0199] The fully connected layer FC2 is implemented by convolution, with an input of 1*1*4096; the activation function is ReLU; some neural nodes are removed in the dropout process to prevent overfitting, and the output of FC2 is 1*1*4096;

[0200] The fully connected layer FC3 is implemented by convolution, with an input of 1*1*4096; the activation function is softmax, the softmax is 2, and the output of FC3 is 1*1*2.

[0201] It should be noted that the specific structure of the above-mentioned artificial intelligence model is only used as a preferred implementation method and does not constitute a specific limitation on the artificial intelligence model of this scheme. In other embodiments adopted in this case, other models may also be used for distribution map recognition.

[0202] Directly calculating and generating symmetric keys based on the chaos principle effectively saves the time of key generation and can effectively increase the number of keys generated. The key calculation is performed in a trusted execution environment, which has the advantages of higher security and higher key generation efficiency.

[0203] Parameter updates are automatically processed using an AI model, but the frequency of updates remains manually controlled. This automated parameter processing is primarily intended to improve model security. When security is guaranteed, parameter updates can be omitted, maintaining stability. Equation parameters can also be specified at the beginning of communication, ensuring security and function performance. According to chaos theory, even if equation parameters are publicly available, the overall system remains secure as long as the initial values ​​are selected securely. However, to enhance overall system security, the ability to update parameters based on the AI ​​model has been added. While this update process does incur some overhead, it operates outside of the communication task and does not impact real-time communication.

[0204] By updating the chaotic function parameters through an AI model, automated processing capabilities are enhanced, making the equation parameters more unpredictable. This also improves the ability to update the equation parameters unsupervised. This simplifies the key generation process and operates within a trusted execution environment, increasing key security and efficiency. The entire solution effectively improves the performance of one-time pads.

[0205] In another embodiment provided by the present invention, the dimension-reduced hyperchaotic function F n =m1X n +m2Y n +m3.

[0206] A dimension-reduced hyperchaotic function can be determined according to the hyperchaotic parameters m1, m2 and m3 in the chaotic parameters.

[0207] In order to solve m1, m2 and m3, we need to n and Y n The distribution diagram of the two two-dimensional hyperchaotic functions is divided into a relatively concentrated area and a relatively discrete area, and the upper and lower boundaries of the relatively concentrated area and the upper and lower boundaries of the relatively discrete area of ​​the distribution diagram of the two two-dimensional hyperchaotic functions are determined.

[0208] Given a numerical range for the corresponding region, assuming that the first two-dimensional hyperchaotic function X n The upper and lower boundaries of the relative gathering area are X 12 and X 11 , the upper and lower boundaries of the discrete region are X 22 and X 21 , the second two-dimensional hyperchaotic function Y n The upper and lower boundaries of the relative gathering area are Y 12 and Y 11 , the upper and lower boundaries of the relative discrete area are Y 22 and Y 21 .

[0209] The converted dimensionality-reduced hyperchaotic function F nThe upper and lower boundaries of the relative gathering area are F 12 and F 11 , the upper and lower boundaries of the relative discrete area are F 22 and F 21 .

[0210] Build F 12 、F 11 、F 22 and F 21 Same as X 22 、X 21 、X 12 、X 11 、Y 22 、Y 21 、Y 12 and Y 11 The functional relationship between them is:

[0211] F 21 =X 21 *m1+Y 21 *m2+m3;

[0212] F 22 =X 22 *m1+Y 22 *m2+m3;

[0213] F 11 =X 11 *m1+Y 11 *m2+m3;

[0214] The boundary of Fn is constrained, and the dimension-reduced hyperchaotic function is mirror-symmetric with respect to the boundary of the discrete region, so: 22 =-F 21 , the lower boundary of the relative clustering area is not less than the upper boundary of the relative discrete area, that is, F 11 ≥F 22 .

[0215] After analysis, only X 11 ≤X 22 , Y 11 ≤Y 22 If

[0216] When X 11 =X 22 And Y 11 =Y 22 When , there is no constraint on m1 and m2;

[0217] When Y 11 ≠Y 22 When m2<((X 11 -X 22 )*m1 / (Y 22 -Y11 ));

[0218] When Y 11 =Y 22 And X 11 ! =X 22 When , m1<0.

[0219] As can be seen from the above constraints, m1 and m2 have opposite sign. When selecting values, the resulting data distribution may not necessarily meet the required diffusion. Considering computational performance, the value can be fine-tuned around 1. After obtaining the one-dimensional generated sequence, the data is normalized and constrained to the interval [0, 1].

[0220] Take X 11 =-0.1, X 12 =0.5, Y 11 =-0.5, Y 12 =0.2,X 21 =-1.2, X 22 =-0.1, Y 21 =0.2, Y 22 =1.5, m1=1, m2=-1, m3=1.5 is calculated. After normalization, we have F n =(X n -Y n +1.5) / 2.5.

[0221] See also Figure 9 , is a distribution diagram of the dimensionality reduction hyperchaotic function provided by an embodiment of the present invention, see Figure 10 , which is a distribution diagram of a one-dimensional logistic function when u=3.9 and the initial value is 0.4, provided by an embodiment of the present invention.

[0222] By comparing the distribution graphs, we can see that the randomness of the distribution of the reduced-dimensional hyperchaotic function is significantly better than that of the one-dimensional logistic function.

[0223] The dimension reduction parameter is determined by solving, and the dimension reduction hyperchaotic function is determined according to the dimension reduction parameter. The randomness of the dimension reduction hyperchaotic function is higher than that of the one-dimensional chaotic function, and the random number generated thereby is more secure.

[0224] In another embodiment provided by the present invention, when determining the upper and lower boundaries of the relatively concentrated area and the upper and lower boundaries of the relatively discrete area, reference is made to X n and Y n The horizontal axis is the number of iterations, and the vertical axis is the distribution value. In order to make a division between the clustered area and the discrete area, the number of data points in each row is selected as the density index for data statistics. When the number of points in each row exceeds a certain threshold, it is used as the dividing boundary. Specifically, X nThe distribution of is used as an example to illustrate the boundary determination method. The process is as follows:

[0225] The maximum value X of the statistical data distribution max and minimum value X min .

[0226] Calculate the maximum value X max and minimum value X min The number of data points at are the maximum number numberX max and the minimum numberX min .

[0227] According to the maximum number numberX max and the minimum numberX min The size relationship between the two groups is used to determine the distribution of relatively concentrated areas and relatively discrete areas;

[0228] If the maximum number numberX max Less than the minimum number numberX min , then the relative clustering area is below the relative discrete area, so let X 12 =X max , X 21 =X min , the minimum value of the distribution graph is taken as the lower boundary of the relative clustering area, and the maximum value of the distribution graph is taken as the upper boundary of the relative discrete area.

[0229] If the maximum number numberX max Equal to the minimum number numberX min When the minimum value is increased and the maximum value is decreased by the preset step size, the maximum and minimum values ​​are updated, and the maximum and minimum quantities are re-determined for judgment.

[0230] If the maximum number numberX max Greater than the minimum number numberX min , then the relative clustering area is above the relative discrete area, so let X 11 =X min , X 22 =X max , the minimum value of the distribution graph is taken as the lower boundary of the relatively discrete area, and the maximum value of the distribution graph is taken as the upper boundary of the relatively concentrated area.

[0231] Then, according to the preset boundary conditions, the boundary value between the relatively discrete area and the relatively concentrated area is searched between the maximum value and the minimum value. The boundary conditions specifically limit the statistical number of distribution points up to the boundary value.

[0232] According to the distribution of the relatively concentrated area and the relatively discrete area, and the location of the dividing point, the upper and lower boundaries of the relatively concentrated area and the upper and lower boundaries of the relatively discrete area are determined.

[0233] When it is necessary to explain, this embodiment uses X n The distribution diagram is used as an example to illustrate the specific method of determining the upper and lower boundaries of the relative cluster area and the upper and lower boundaries of the relative discrete area. n The distribution graph can also be solved using the same solution.

[0234] In another embodiment provided by the present invention, when searching for the boundary value between the relatively discrete area and the relatively concentrated area, when the relatively concentrated area is located below the relatively discrete area, the following steps are specifically performed:

[0235] That is, the maximum number numberX max Less than the minimum number numberX min When the relative clustering area is below the relative discrete area, the preset first step length d is selected and the current judgment value X is set to new =X min +d, statistics of current judgment value X new The number of data points in the row, that is, the current judgment quantity numberX new , determine the current judgment number numberX new Is it greater than the minimum number numberX min ;

[0236] If the current judgment quantity numberX new ≤ minimum quantity numberX min , then calculate the demarcation ratio T=(numberX min -numberX new ) / numberX min , when T≤50%, the current judgment quantity numberX new As the previous judgment quantity numberX new1 , continue to increase X new The value of X new =X new +d, recalculate the demarcation ratio T = (numberX new1 -numberX new ) / numberX new1 , when T>50%, it is considered that X new It is the dividing point between relatively concentrated areas and relatively discrete areas.

[0237] If the current judgment quantity numberX new Minimum number X min, then the current judgment quantity numberX new As the previous judgment quantity numberX new1 , continue to increase X new The value of X new =X new +d; statistics of current judgment value X new The number of data points in the row, that is, the current judgment quantity numberX new , determine the current judgment number numberX new Is it greater than the previous judgment quantity numberX new1 .

[0238] If the current judgment quantity numberX new Greater than the previous judgment quantity numberX new1 , then calculate the demarcation ratio T=(numberX new1 -numberX new ) / numberX new1 , when T≤50%, the current judgment quantity numberX new As the previous judgment quantity numberX new1 , continue to increase X new The value of X new =X new +d, recalculate the demarcation ratio T = (numberX new1 -numberX new ) / numberX new1 , when T>50%, it is considered that X new It is the dividing point between relatively concentrated areas and relatively discrete areas.

[0239] When X new When numberX is the dividing point, max <numberX min In this case, let X 22 =X 11 =X new , then the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area are determined accordingly.

[0240] In another embodiment provided by the present invention, when searching for the boundary value between the relatively discrete area and the relatively concentrated area, when the relatively concentrated area is located above the relatively discrete area, the following steps are specifically performed:

[0241] That is, the maximum number numberX max Greater than the minimum number numberX min When the relative clustering area is above the relative discrete area, the preset second step length k is selected and the current judgment value X is set to new=X ma -k, count the current judgment value X new The number of data points in the row, that is, the current judgment quantity numberX new , determine the current judgment number numberX new Is it greater than the maximum number numberX max ;

[0242] If the current judgment quantity numberX new ≤maximum number numberX max , then calculate the demarcation ratio T=(numberX max -numberX new ) / numberX max , when T≤50%, the current judgment quantity numberX new As the previous judgment quantity numberX new1 , continue to reduce X new The value of X new =X new -k, recalculate the demarcation ratio T = (numberX new1 -numberX new ) / numberX new1 , when T>50%, it is considered that X new It is the dividing point between relatively concentrated areas and relatively discrete areas.

[0243] If the current judgment quantity numberX new > Maximum number numberX max , then the current judgment quantity numberX new As the previous judgment quantity numberX new1 , continue to reduce X new The value of X new =X new -k; statistics of current judgment value X new The number of data points in the row, that is, the current judgment quantity numberX new , determine the current judgment number numberX new Is it greater than the previous judgment quantity numberX new1 .

[0244] If the current judgment quantity numberX new Greater than the previous judgment quantity numberX new1 , then calculate the demarcation ratio T=(numberX new1 -numberX new ) / numberX new1, when T≤50%, the current judgment quantity numberX new As the previous judgment quantity numberX new1 , continue to reduce X new The value of X new =X new -k, recalculate the demarcation ratio T = (numberX new1 -numberX new ) / numberX new1 , when T>50%, it is considered that X new It is the dividing point between relatively concentrated areas and relatively discrete areas.

[0245] When X new When numberX is the dividing point, max >numberX min In this case, let X 21 =X 12 =X new , then the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area are determined accordingly.

[0246] See also Figure 11 , is a schematic structural diagram of a hyperchaotic key one-time pad transmission device provided by an embodiment of the present invention, the device comprising:

[0247] A symmetric module is used to decrypt the obtained chaotic parameters and large prime number parameters using the generated symmetric key;

[0248] A chaos module, configured to determine a chaos function according to the chaos parameters, and generate a random number sequence using the chaos function;

[0249] A key generation module, configured to generate a key stream based on the generated random number sequence and the large prime number parameter;

[0250] The transmission module is used to perform one-time pad data transmission according to the key stream.

[0251] The hyperchaotic key one-time pad transmission device provided in this embodiment can execute all the steps and functions of the hyperchaotic key one-time pad transmission method provided in any of the above embodiments, and the specific functions of the device will not be described in detail here.

[0252] See also Figure 12 , is a schematic diagram of the structure of a terminal device provided by an embodiment of the present invention. The terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a hyperchaotic key one-time pad transmission program. When the processor executes the computer program, it implements the steps of each of the above-mentioned embodiments of the hyperchaotic key one-time pad transmission method, such as Figure 1Alternatively, the processor implements the functions of the modules in the above-mentioned device embodiments when executing the computer program.

[0253] Exemplarily, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the computer program can be divided into several modules, and the specific functions of each module have been described in detail in the hyperchaotic key one-time pad transmission method provided in any of the above embodiments. The specific functions of this device will not be repeated here.

[0254] The terminal device may be a computing device such as a desktop computer, laptop, PDA, or cloud server. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0255] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0256] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the hyperchaotic key one-time pad transmission device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0257] If the module integrated into the hyperchaotic key one-time pad transmission device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process of the above-mentioned method embodiment by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium.

[0258] An embodiment of the present invention further provides a computer program product, comprising a computer program / instruction, which implements the steps of any of the methods described in the above embodiments when executed by a processor.

[0259] It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A one-time-one-pad transmission method for a hyperchaotic key, characterized in that: The method comprises: Use the generated symmetric key to decrypt the obtained chaotic parameters and large prime number parameters; Determining a chaotic function according to the chaotic parameters, and generating a random number sequence using the chaotic function; Generate a key stream according to the generated random number sequence and the large prime number parameter; One-time pad data transmission is performed according to the key stream.

2. The hyperchaotic key one-time pad transmission method according to claim 1, characterized in that: Determining a chaotic function according to the chaotic parameter, and using the chaotic function to generate a random number sequence, comprising: Determining a one-dimensional chaotic function according to a one-dimensional branch parameter in the chaotic parameters, and determining a reduced-dimensional hyperchaotic function according to a hyperchaotic parameter in the chaotic parameters; Using the symmetric key as the initial value of one-dimensional chaos, iterating the one-dimensional chaotic function a preset number of times to determine an output sequence; The initial value of the hyperchaos is determined according to the output sequence, and the determined initial value is input into the dimension reduction hyperchaos function to generate the random number sequence.

3. The hyperchaotic key one-time pad transmission method according to claim 1, characterized in that: The generation process of the symmetric key includes: True random numbers generated by SRAM PUF in a trusted execution environment; The symmetric key is generated according to the true random number.

4. The hyperchaotic key one-time pad transmission method according to claim 2, characterized in that: Determining an initial value of hyperchaos according to the output sequence includes: The initial value of the hyperchaos is selected from the output sequence in a reverse order manner.

5. The hyperchaotic key one-time pad transmission method according to claim 1, characterized in that: Performing one-time pad data transmission according to the key stream includes: storing the keystream in a trusted execution environment; When the data to be encrypted is sensitive data, encrypting the data to be encrypted using the key stream in the trusted execution environment; When the data to be encrypted is not sensitive data, the key stream is taken out from the trusted execution environment, and the data to be encrypted is encrypted using the key stream in a conventional environment.

6. The hyperchaotic key one-time pad transmission method according to claim 2, characterized in that: The process of determining the hyperchaotic parameters specifically includes: Selecting preliminary values ​​of two-dimensional parameters of two preset two-dimensional hyperchaotic functions; Determine the dimension reduction parameter of the dimension reduction hyperchaotic function according to the distribution diagrams of the two two-dimensional hyperchaotic functions, and obtain the dimension reduction hyperchaotic function; Identifying the distribution graph of the dimensionality-reduced hyperchaotic function according to a pre-trained artificial intelligence model; When the recognition result is an invalid graph, adjusting the two-dimensional branch parameters in the two-dimensional parameters within a preset range, re-determining the dimension reduction hyperchaotic function, and identifying the distribution graph of the re-determined dimension reduction hyperchaotic function; When the recognition result is a valid graph, the latest two-dimensional parameters are used as the hyperchaotic parameters.

7. The hyperchaotic key one-time pad transmission method according to claim 6, characterized in that: According to the distribution diagrams of the two two-dimensional hyperchaotic functions, the dimension reduction parameters of the dimension reduction hyperchaotic function are determined, and the dimension reduction hyperchaotic function is obtained, including: Determine the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area of ​​the distribution diagrams of two two-dimensional hyperchaotic functions; Constructing a functional relationship between the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the dimensionality reduction hyperchaotic function and the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the distribution graphs of the two two-dimensional hyperchaotic functions, and the dimensionality reduction parameters; The functional relationship is solved according to the boundary constraints of the dimension-reduced hyperchaotic function to determine the dimension-reduced parameters, and the dimension-reduced hyperchaotic function is determined according to the dimension-reduced parameters.

8. The hyperchaotic key one-time pad transmission method according to claim 7, characterized in that: Determining the upper and lower boundaries of the relative aggregation area and the upper and lower boundaries of the relative discrete area of ​​the distribution graphs of two two-dimensional hyperchaotic functions includes: For different two-dimensional hyperchaotic functions, the minimum number of minimum value distribution points and the maximum number of maximum value distribution points in the distribution graph of the two-dimensional hyperchaotic function are counted respectively; Determining the distribution of relatively concentrated areas and relatively discrete areas according to the size relationship between the maximum number and the minimum number; A demarcation value that meets the preset demarcation condition is searched between the maximum value and the minimum value as the demarcation point between the relative clustering area and the relative discrete area, and the upper and lower boundaries of the relative clustering area and the upper and lower boundaries of the relative discrete area are determined.

9. The hyperchaotic key one-time pad transmission method according to claim 8, characterized in that: Searching for a demarcation value that meets a preset demarcation condition between the maximum value and the minimum value includes: When the maximum number is less than the minimum number, the minimum value is increased by a preset first step length, the current judgment value is updated, the current judgment number of the current judgment value is counted, and it is determined whether the current judgment number is greater than the minimum number; If so, take the current judgment number as the previous judgment number, increase the current judgment value by the first step length, update the current judgment value, re-count the current judgment number, and determine whether the current judgment number is greater than the previous judgment number; If not, calculate the ratio of the difference between the previous judgment quantity and the current judgment quantity to the cutoff value of the previous judgment quantity; When the demarcation ratio is not greater than a preset first threshold, the current judgment quantity is used as the previous judgment quantity, the current judgment value is increased by the step size, the current judgment value is updated, the current judgment quantity is recounted, and the demarcation ratio is recalculated; When the demarcation ratio is greater than the first threshold, the current judgment value is used as the demarcation value.

10. The hyperchaotic key one-time pad transmission method according to claim 8, characterized in that: Searching for a demarcation value that meets a preset demarcation condition between the maximum value and the minimum value includes: When the maximum number is greater than the minimum number, reducing the maximum value by a preset second step length, updating the current judgment value, counting the current judgment number of the current judgment value, and determining whether the current judgment number is greater than the maximum number; If so, taking the current judgment number as the previous judgment number, reducing the current judgment value by the second step length, updating the current judgment value, re-counting the current judgment number, and determining whether the current judgment number is greater than the previous judgment number; If not, calculate the ratio of the difference between the previous judgment quantity and the current judgment quantity to the cutoff value of the previous judgment quantity; When the demarcation ratio is not greater than a preset second threshold, the current judgment quantity is used as the previous judgment quantity, the current judgment value is reduced by the step size, the current judgment value is updated, the current judgment quantity is recounted, and the demarcation ratio is recalculated; When the demarcation ratio is greater than the second threshold, the current judgment value is used as the demarcation value.

11. A hyperchaotic key one-time pad transmission device, characterized in that: The device comprises: A symmetric module is used to decrypt the obtained chaotic parameters and large prime number parameters using the generated symmetric key; A chaos module, configured to determine a chaos function according to the chaos parameters, and generate a random number sequence using the chaos function; A key generation module, configured to generate a key stream based on the generated random number sequence and the large prime number parameter; The transmission module is used to perform one-time pad data transmission according to the key stream.

12. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for transmitting a hyperchaotic key one time pad as described in any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the hyperchaotic key one-time pad transmission method according to any one of claims 1 to 10.

14. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

Images