An encryption method and decryption method for vehicle networking data

Abstract

The present application relates to the technical field of information security, and especially relates to a kind of vehicle networking data encryption method and decryption method, the encryption method includes: obtaining the image of vehicle networking data, and determine the sensitive area of image;Randomly generate the parameter of specialized two-dimensional Logistic chaotic encrypter chaotic sequence and AES key;Based on the parameter of specialized two-dimensional Logistic chaotic encrypter chaotic sequence, sensitive area is encrypted, and encrypted image is obtained;Based on AES key, the parameter of chaotic sequence is encrypted, and encrypted chaotic parameter is obtained;Based on sensitive area and AES key, signature data is obtained;Based on encrypted chaotic parameter and signature data, embedded data is obtained;Embedded data is dynamically embedded in encrypted image, and the encryption of vehicle networking data is realized;Shamir key distribution method is used to fragment AES key, and distribute to the main body of car enterprise, traffic management department and insurance company, realize data encryption intensity and safe transmission, promote the safe circulation and value release of vehicle networking data element.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and in particular to a method for encrypting and decrypting vehicle network data. Background Technology

[0002] With the large-scale deployment of intelligent connected vehicles, massive amounts of image and video data are frequently flowing between vehicles, roadside, and the cloud. This data contains sensitive information such as vehicle identification, facial recognition, and geographic location, which faces serious risks of leakage and misuse. Existing data anonymization solutions have significant shortcomings: traditional mosaic or blurring methods are irreversible, causing the data to lose its value for subsequent analysis and evidence; while conventional encryption methods lack cross-institutional collaborative decryption capabilities, making it difficult to meet the complex needs of secure sharing and compliant use among multiple stakeholders in the connected vehicle ecosystem, including automakers, traffic management departments, insurance companies, and users. Especially in scenarios such as accident tracing, insurance claims assessment, and traffic enforcement, there is a need to strictly protect privacy while efficiently restoring the original data under authorized conditions; current technologies have not yet achieved this balance.

[0003] Therefore, how to provide an encryption and decryption method for vehicle network data that can ensure data encryption strength and secure transmission while also allowing for flexible decryption is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] In view of the above problems, the present invention provides an encryption and decryption method for vehicle network data that overcomes or at least partially solves the above problems.

[0005] In a first aspect, the present invention provides a method for encrypting vehicle network data, comprising:

[0006] Acquire images of vehicle-to-everything (V2X) data and determine the sensitive areas of the images;

[0007] Randomly generate the chaotic sequence parameters and AES key of a specialized two-dimensional Logistic chaotic cipher;

[0008] Based on the chaotic sequence parameters, the sensitive region is encrypted to obtain an encrypted image;

[0009] Based on the AES key, the chaotic sequence parameters are encrypted to obtain encrypted chaotic parameters;

[0010] Based on the sensitive area and the AES key, the signature data is obtained;

[0011] Based on the encrypted chaotic parameters and the signature data, the embedded data is obtained;

[0012] The embedded data is embedded into the encrypted image to achieve encryption of vehicle network data;

[0013] The AES key is fragmented using the Shamir key distribution method and distributed to the main entities, including car manufacturers, traffic management departments, and insurance companies.

[0014] Preferably, based on the chaotic sequence parameters, the sensitive region is encrypted to obtain an encrypted image, including:

[0015] Obtain the location information of the sensitive area and mark it in the least significant bit of the image;

[0016] Based on the location information of the sensitive region, the length of the required chaotic sequence is determined;

[0017] Based on the length, the corresponding Logistic chaotic sequence is obtained;

[0018] Based on the Logistic chaotic sequence, the sensitive region is encrypted to obtain an encrypted image.

[0019] Preferably, the sensitive region is encrypted based on the Logistic chaotic sequence to obtain an encrypted image, including:

[0020] After sorting the Logistic chaotic sequence, a mapping table is obtained;

[0021] Based on the mapping table, the original pixels of the sensitive area are shuffled to obtain disordered pixels;

[0022] Based on the Logistic chaotic sequence corresponding to the chaotic pixels and the original pixels, pixel values ​​are replaced through XOR operation to obtain an encrypted image.

[0023] Preferably, the chaotic sequence parameters are encrypted using an AES key to obtain encrypted chaotic parameters, including:

[0024] The chaotic sequence parameters are packaged to obtain 128 bits of packaged data;

[0025] Based on the packaged data, the encrypted chaotic parameters are obtained by encrypting it with an AES key.

[0026] Preferably, based on the sensitive area and the AES key, signature data is obtained, including:

[0027] The sensitive area is processed using the SM3 cryptographic hash algorithm to generate 256-bit hash data;

[0028] Based on the 256-bit hash data, HMAC-SM3 signature is performed using an AES key to obtain the signature data.

[0029] Preferably, based on the cryptographic chaotic parameters and the signature data, the embedded data is obtained, including:

[0030] The encrypted chaotic parameters are concatenated with the signature data to obtain the embedded data.

[0031] Secondly, the present invention also provides a method for decrypting vehicle network data, comprising:

[0032] Obtain the image to be decrypted;

[0033] Upon receiving recovery requests initiated by each entity, the AES key is determined based on the recovery requests from each entity using the Shamir key recovery method. The recovery requests include AES key fragments corresponding to each entity.

[0034] Based on the image to be decrypted, extract the embedded data and the encrypted image;

[0035] Disassemble the embedded data to obtain encrypted chaotic parameters and signature data;

[0036] Based on the AES key and the encryption chaos parameters, the chaos sequence parameters of the specialized two-dimensional Logistic chaotic encryptor are obtained;

[0037] Based on the chaotic sequence parameters, the encrypted image is decrypted to obtain the original image, thus realizing the decryption of the image to be decrypted.

[0038] Preferably, after extracting the embedded data using the inverse process of the histogram-shifting RDH data extraction algorithm based on the image to be decrypted, the method further includes:

[0039] The signature data is verified using HMAC-SM3 signature technology to determine the security of the image to be decrypted.

[0040] Preferably, after disassembling the embedded data to obtain the encrypted chaotic parameters and signature data, the method further includes:

[0041] Obtain the least significant bit of the encrypted image;

[0042] Based on the least significant bit, the location information of the sensitive area of ​​the encrypted image is determined.

[0043] Preferably, the encrypted image is decrypted based on the chaotic sequence parameters to obtain the original image, including:

[0044] Based on the chaotic sequence parameters and the location information of the sensitive region of the encrypted image, a Logistic chaotic sequence is obtained;

[0045] Based on the aforementioned Logistic chaotic sequence, an inverse mapping table is obtained;

[0046] Based on the inverse mapping table, the pixels in the sensitive area are subjected to an inverse XOR operation and then inversely replaced to obtain the original image.

[0047] Thirdly, the present invention also provides an encryption device for vehicle network data, comprising:

[0048] The first acquisition module is used to acquire images related to vehicle network data and determine the sensitive areas of the images;

[0049] The generation module is used to randomly generate the chaotic sequence parameters and AES key of a specialized two-dimensional Logistic chaotic cipher.

[0050] The first obtaining module is used to encrypt the sensitive region based on the chaotic sequence parameters to obtain an encrypted image;

[0051] The second obtaining module is used to encrypt the chaotic sequence parameters based on the AES key to obtain encrypted chaotic parameters;

[0052] The third module is used to obtain signature data based on the sensitive area and the AES key;

[0053] The fourth module is used to obtain embedded data based on the encrypted chaotic parameters and the signature data;

[0054] An embedding module is used to embed the embedded data into the encrypted image to achieve encryption of vehicle network data;

[0055] The distribution module is used to fragment the AES key using the Shamir key distribution method and distribute it to the main entities, including car manufacturers, traffic management departments, and insurance companies.

[0056] Fourthly, the present invention also provides a device for decrypting vehicle network data, such as... Figure 10 As shown, it includes:

[0057] The second acquisition module is used to acquire the image to be decrypted;

[0058] The recovery module is used to determine the AES key based on the recovery request initiated by each subject by using the Shamir key recovery method. The recovery request includes AES key fragments corresponding to each subject.

[0059] The extraction module is used to extract embedded data and encrypted images based on the image to be decrypted;

[0060] The disassembly module is used to disassemble the embedded data to obtain encrypted chaotic parameters and signature data;

[0061] The fifth module is used to obtain the chaotic sequence parameters of a specialized two-dimensional Logistic chaotic encryptor based on the AES key and the encryption chaotic parameters.

[0062] The decryption module is used to decrypt the encrypted image based on the chaotic sequence parameters to obtain the original image, thereby realizing the decryption of the image to be decrypted.

[0063] Fifthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first or second aspect.

[0064] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0065] This invention provides a method for encrypting vehicle-to-everything (V2X) data, comprising: acquiring an image of the V2X data and determining the sensitive region of the image; randomly generating a specialized two-dimensional Logistic chaotic encryptor chaotic sequence parameter and an AES key; encrypting the sensitive region based on the specialized two-dimensional Logistic chaotic encryptor chaotic sequence parameter to obtain an encrypted image; encrypting the chaotic sequence parameter based on the AES key to obtain encrypted chaotic parameters; obtaining signature data based on the sensitive region and the AES key; obtaining embedded data based on the encrypted chaotic parameters and the signature data; dynamically embedding the embedded data into the encrypted image to achieve encryption of the V2X data; and using the Shamir key distribution method to fragment the AES key and distribute it to vehicle manufacturers, traffic management departments, and insurance companies to achieve data encryption strength and secure transmission, thereby promoting the secure circulation and value release of V2X data elements. Attached Figure Description

[0066] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0067] Figure 1 This invention illustrates a flowchart of the steps involved in encrypting vehicle network data according to an embodiment of the present invention.

[0068] Figure 2 This diagram illustrates the encryption process for sensitive areas in an embodiment of the present invention.

[0069] Figure 3 A schematic diagram of the data embedding process in an embodiment of the present invention is shown;

[0070] Figure 4This diagram illustrates the process of fragmenting an AES key using the Shamir key allocation method in an embodiment of the present invention.

[0071] Figure 5 This invention illustrates a flowchart of the steps involved in decrypting vehicle network data according to an embodiment of the present invention.

[0072] Figure 6 This diagram illustrates the recovery of the AES key in an embodiment of the present invention.

[0073] Figure 7 A schematic diagram of the signature verification process in an embodiment of the present invention is shown;

[0074] Figure 8 A schematic diagram of the image decryption process in an embodiment of the present invention is shown;

[0075] Figure 9 A schematic diagram of the structure of the encryption device for vehicle network data in an embodiment of the present invention is shown;

[0076] Figure 10 A schematic diagram of the decryption device for vehicle network data in an embodiment of the present invention is shown. Detailed Implementation

[0077] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0078] Example 1:

[0079] Embodiments of the present invention provide a method for encrypting vehicle network data, such as... Figure 1 As shown, it includes:

[0080] S101, acquire images of vehicle network data and determine sensitive areas of the images;

[0081] S102, randomly generate the chaotic sequence parameters and AES key of a specialized two-dimensional Logistic chaotic cipher;

[0082] S103, based on chaotic sequence parameters, the sensitive area is encrypted to obtain an encrypted image;

[0083] S104, based on the AES key, encrypt the chaotic sequence parameters to obtain encrypted chaotic parameters;

[0084] S105, based on location information and AES key, obtain signature data;

[0085] S106, based on the encrypted chaotic parameters and signature data, the embedded data is obtained;

[0086] S107, dynamically embed the embedded data into the encrypted image to achieve encryption of vehicle network data;

[0087] S108 uses the Shamir key distribution method to fragment the AES key and distribute it to the main entities, including car manufacturers, traffic management departments, and insurance companies.

[0088] By adopting this encryption method, the security, reliability, and recoverability of vehicle network data during transmission, storage, and sharing are significantly improved.

[0089] Next, the overall plan will be described in detail:

[0090] First, S101, acquire images of vehicle network data and determine the sensitive areas of the images.

[0091] An image related to vehicle-to-everything (V2X) data may contain sensitive information such as license plate numbers, faces, and geographic locations. Image recognition technology is used to identify sensitive areas within the image. These sensitive areas include license plate numbers, faces, and geographic locations, etc. This image of the V2X data is denoted as P.

[0092] Next, S102 is executed to randomly generate the chaotic sequence parameters and AES key of a specialized two-dimensional Logistic chaotic cipher.

[0093] AES keys can be of various lengths. To ensure the convenience and speed of the entire encryption process, this invention uses the AES-128-bit mode for overall operation, and the generated AES key is denoted as Kaes.

[0094] The chaotic sequence parameters of the specialized two-dimensional Logistic chaotic cipher specifically include the basic parameters r and X0. These parameters satisfy the following condition:

[0095] 0 < X0 < 1 and 3.56995 < r ≤ 4.

[0096] The choice of r is crucial, directly affecting the chaos intensity of the Logistic chaotic sequence. The theoretical starting point of chaos is 3.56995, and 3.56995 is the theoretical threshold for the start of chaos (i.e., the Feigenbaum point), which is also the critical point for the system to enter chaos from the periodic doubling bifurcation.

[0097] As r approaches 3.56995, the system may still exhibit short-period or quasi-periodic behavior (incomplete chaos), and the degree of chaos increases with the increase of r.

[0098] To enhance the chaos intensity, r should theoretically be chosen to approach a value of 4. However, to ensure security, the range of r is expanded, adopting a chaos intensity range of 3.7 to 4. This range is also the recommended range for applying logistic chaotic sequences in the field of encryption.

[0099] The random number generation method uses the system-based cryptographically secure pseudo-random number generation technique CSPRNG. Specifically, the generation method can use the urandom function of Python's os library or its wrapper library secrets for secure pseudo-random generation, avoiding the use of the random library. The pseudocode is as follows:

[0100] import secrets

[0101] # Generate AES-128 key (16 bytes)

[0102] aes_key = secrets.token_bytes(16)

[0103] print("AES-128 Key:", aes_key.hex())

[0104] # Generate Logistic parameters

[0105] x0 = secrets.randbits(32) / (2**32) # X0 ∈ (0, 1)

[0106] r = 3.7 + secrets.randbits(32) 0.3 / (2 **32) # r ∈ (3.7, 4.0]

[0107] print(f"Logistic x0: {x0:.6f}")

[0108] print(f"Logistic r: {r:.6f}")

[0109] Next, step S103 is executed, and based on the chaotic sequence parameters, the sensitive area is encrypted to obtain an encrypted image.

[0110] Specifically, the location information of the sensitive area is obtained and marked in the least significant bits of the image;

[0111] Based on the location information of the sensitive region, determine the length of the required chaotic sequence;

[0112] Based on this length, the corresponding Logistic chaotic sequence is obtained;

[0113] Based on the Logistic chaotic sequence, the sensitive area is encrypted to obtain an encrypted image.

[0114] The encryption process is as follows:

[0115] First, sort the Logistic chaotic sequence to obtain the mapping table;

[0116] Based on the mapping table, the original pixels in the sensitive area are scrambled to obtain disordered pixels;

[0117] Based on the Logistic chaotic sequence corresponding to the chaotic pixels and the original pixels, pixel values ​​are replaced through XOR operation to obtain the encrypted image.

[0118] The following is a detailed description of each step:

[0119] First, when acquiring the location information of sensitive regions, a Haar cascade classifier or a YOLOv8 model combined with PaddleOCR is used for acquisition and labeling. This allows for the rapid identification of sensitive regions, such as facial information.

[0120] Next, the location information of the sensitive area is specifically the location information of the pixels. For example, the location information of the obtained sensitive information is as follows:

[0121] [[a,b],[c,b],[c,d],[a,d]]

[0122] This location information is the coordinates of the boundary vertices of the sensitive area, and the number of pixels containing the sensitive information is determined within the range of these boundary vertices.

[0123] The location information is marked in the least significant bit of the image, i.e., LSB embedding is performed. Using LSB embedding satisfies the security and reversibility requirements of the scene.

[0124] Then, based on this location information, the required length of the chaotic sequence is determined. Specifically, the required length of the chaotic sequence is determined based on the number of pixels n in the sensitive region. That is, the number of pixels n in the sensitive region is equal to the required length of the chaotic sequence.

[0125] Next, based on this length, the corresponding Logistic chaotic sequence is obtained. The specific formula is as follows:

[0126] X n =r*X n-1 *(1- X n-1 )

[0127] Following this formula, the corresponding Logistic chaotic sequence is obtained, denoted as X. n ~X0.

[0128] The sensitive region is encrypted using this Logistic chaotic sequence to obtain the encrypted image, as follows:

[0129] First, sort according to the actual Logistic chaotic sequence, denoted as sort(X). n ~ X0), for example, after sorting, it becomes [X8, X 56 X 887 ..X 999 A mapping table is obtained, which is [8, 56, 887…999]. Based on this mapping table, the original pixels in the sensitive area are scrambled to obtain disordered pixels. That is, the first pixel of the original pixel is placed at the 8th pixel position, the second pixel is placed at the 56th pixel position, and so on, to obtain scrambled pixels. Then, based on the Logistic chaotic sequence corresponding to the disordered pixels and the original pixels, a pixel value substitution is achieved through an XOR operation to obtain the encrypted image.

[0130] Specifically, the formula is as follows:

[0131] E k =R k ∧ round(X k *255)

[0132] Among them, R k X is the pixel value of the original pixel. k This represents the value of the Logistic chaotic sequence corresponding to the disordered pixels, with `round` rounding the result to the nearest integer. ∧ E represents the XOR operation. k The value after replacing the pixel value.

[0133] A chaotic sequence parameter is used to encrypt the sensitive region. Through confusion and diffusion, it exhibits a strong chaotic effect, providing an avalanche effect. The resulting encrypted image is denoted as Encode(P). Figure 2 The diagram illustrates the encryption process for sensitive areas.

[0134] Next, decryptable data is embedded in the encrypted image. This decryptable data includes chaotic sequence parameters and the AES key.

[0135] Therefore, S104 is executed to encrypt the chaotic sequence parameters based on the AES key, resulting in encrypted chaotic parameters.

[0136] Specifically, the chaotic sequence parameters are packaged to obtain 128 bits of packaged data;

[0137] Based on the packaged data, AES key encryption is used to obtain encrypted chaotic parameters.

[0138] The chaotic sequence parameter is a 6-bit floating-point number. The byte stream is converted and packed using the `pack` function from Python's `struct` library. `struct.pack` is a Python function used to pack data into binary format. The specific pseudocode is as follows:

[0139] import struct

[0140] data = struct.pack('dd', x0, r)

[0141] The packaged data is 16 bytes, which meets the AES-128 encryption requirements.

[0142] Of course, other packaging methods can also be used, and no restrictions are imposed here.

[0143] Next, we use an AES key to encrypt the data and obtain the encrypted chaotic parameters, as shown in the pseudocode below:

[0144] iv=os.urandom(16)

[0145] EncodeData= AES.new(kaes, AES.MODE_CBC, iv).encrypt(data)

[0146] EncodeParameters=iv||EncodeData

[0147] The resulting EncodeParameters is 32 bytes of data, including the iv and the encrypted data, where the iv is a random or pseudo-random number.

[0148] Specifically, based on the packaged data, AES key encryption is used to obtain the encrypted chaotic parameters. The specific process is as follows:

[0149] When randomly generating the AES key, IV data is also randomly generated. The IV data is also 128 bits. The 128-bit packed data (x0 and r) is XORed with the IV data and then filled into a 4×4 byte matrix. The AES key is then expanded into an 11-round key. The 4×4 byte matrix is ​​then encrypted with round function for 11 rounds in sequence, thus obtaining the encryption chaos parameters.

[0150] In S105, signature data is obtained based on the sensitive area and the AES key.

[0151] Specifically, the SM3 cryptographic hash algorithm is used to process the encrypted image to generate 256-bit hash data, as shown in the following formula:

[0152] ImageHash=SM3(Encode(P))

[0153] The process of processing sensitive areas using the SM3 cryptographic hash algorithm is as follows:

[0154] First, the private region of the encrypted image is converted into a binary bitstream. Next, the binary bitstream is padded with messages to make its length a multiple of 512 bits. Then, message expansion and compression operations are performed. Finally, after 64 rounds of iterative operations, a 256-bit hash data is generated.

[0155] After obtaining the 256-bit hash data, an HMAC-SM3 signature is performed using the AES key to obtain the signature data. Specifically, the hmac and gmssl extension libraries of Python are used. The pseudocode is as follows:

[0156] import hmac

[0157] from gmssl import sm3

[0158] HmacData=hmac.new(Kaes, ImageHash, sm3).digest()

[0159] Therefore, the HmacData data should be 256 bits, or 32 bytes.

[0160] The specific process is as follows:

[0161] The AES key is padded with zeros to obtain 512 bits, denoted as K. Two mask constants are defined: an inner mask `ipad` and an outer mask `opad`. First, the inner mask is XORed with the AES key K, and then 256 bits of hash data are appended to it to obtain intermediate data. Then, the outer mask `opad` is XORed with the AES key K, and the intermediate value is appended to it. Finally, an SM3 cryptographic hash operation is performed to output the final signature data.

[0162] This signature data ensures the integrity and authenticity of the encrypted image during transmission and storage.

[0163] Next, S106 is executed to obtain the embedded data based on the encrypted chaotic parameters and signature data.

[0164] Specifically, the encrypted chaotic parameters and signature data are concatenated to obtain the embedded data. The specific formula is as follows:

[0165] PackData =EncodeParameters+HmacData

[0166] This embedded data is denoted as PackData.

[0167] Finally, S107 is executed to dynamically embed the embedded data into the encrypted image, thereby encrypting the vehicle network data.

[0168] Specifically, the histogram-shifting RDH technique is used to dynamically embed the image. The specific formula is as follows:

[0169] EndImage=RDH(Encode(P),PackData)

[0170] Specifically, the encrypted image is first converted into a grayscale image. Then, based on the pixel value distribution of the grayscale image, a histogram is generated, thus obtaining peak points (the most frequently occurring pixel values) and zero points (pixel values ​​with a frequency of 0). Next, the histogram blocks near the peak points are shifted towards the zero point, and the shift amount is recorded. The empty positions are then used to embed the embedded data, enabling dynamic embedding of the embedded data into the encrypted image and achieving encryption of vehicle network data.

[0171] This completes the image encryption and data embedding operations. The processes S105~S107 are as follows: Figure 3 As shown.

[0172] Of course, key protection is also required. Therefore, S108 is executed to use the Shamir key distribution method to fragment the AES key and distribute it to the main entities of car manufacturers, traffic management departments and insurance companies.

[0173] Among them, the Shamir key distribution method is a classic method of threshold cryptography. Based on the principle of polynomial interpolation for key segmentation and reconstruction, it can securely perform key fragmentation operations, distributing fragments to various subjects. Only when the corresponding threshold value is reached can the key be recovered.

[0174] The original key can be divided into several independent fragments, and a threshold value t can be set so that the original key can be recovered if and only if at least t fragment keys are combined, while any combination of fewer than t fragment keys cannot derive the original key.

[0175] The specific formula is as follows:

[0176] K1, K2, K3....Kn=Shamir(Kaes).

[0177] Distribute K1, K2, and K3 to their respective subjects. For example... Figure 4 The diagram illustrates the process of fragmenting an AES key using the Shamir key distribution method.

[0178] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0179] This invention provides a method for encrypting vehicle-to-everything (V2X) data, comprising: acquiring an image of V2X data and determining the sensitive region of the image; randomly generating a chaotic sequence parameter and an AES key for a specialized two-dimensional Logistic chaotic encryptor; encrypting the sensitive region based on the chaotic sequence parameter to obtain an encrypted image; encrypting the chaotic sequence parameter using AES to obtain encrypted chaotic parameters; obtaining signature data based on the sensitive region and the AES key; obtaining embedded data based on the encrypted chaotic parameters and the signature data; dynamically embedding the embedded data into the encrypted image to achieve encryption of the V2X data; and using the Shamir key distribution method to fragment the AES key and distribute it to vehicle manufacturers, traffic management departments, and insurance companies to achieve data encryption strength and secure transmission, thereby promoting the secure circulation and value release of V2X data elements.

[0180] Example 2:

[0181] Based on the same inventive concept, this invention also provides a method for decrypting vehicle network data, such as... Figure 5 As shown, it includes:

[0182] S501, Obtain the image to be decrypted;

[0183] S502, upon receiving recovery requests initiated by each subject, the AES key is determined based on the recovery requests of each subject using the Shamir key recovery method, wherein the recovery request includes AES key fragments corresponding to each subject;

[0184] S503, based on the image to be decrypted, extracts embedded data and encrypted images;

[0185] S504, disassemble the embedded data to obtain encrypted chaotic parameters and signature data;

[0186] S505, based on the AES key and the encryption chaos parameters, obtains the chaos sequence parameters of a specialized two-dimensional Logistic chaotic cipher;

[0187] S506 decrypts the encrypted image based on chaotic sequence parameters to obtain the original image, thus realizing the decryption of the encrypted image.

[0188] First, S501, acquire the image to be decrypted. Sensitive areas in this image have already been encrypted.

[0189] Next, in step S502, when a recovery request is received from each subject, the AES key is determined based on the recovery request from each subject using the Shamir key recovery method. The recovery request includes the AES key fragments corresponding to each subject.

[0190] The inverse process of the Shamir key distribution method, namely the Shamir key recovery method, is described in the following formula:

[0191] Shamir(K1,K2,K3...Kn)=Kaes

[0192] This yields the AES key. The AES key can then be recovered by determining the number of threshold values ​​that are satisfied. For example... Figure 6 The diagram shown illustrates the recovery of the AES key.

[0193] Then, S503 is executed to extract the embedded data and encrypted image based on the image to be decrypted. Specifically, it is the inverse process of the histogram-shifted RDH data extraction algorithm.

[0194] The histogram-shifted RDH data extraction algorithm is a reversible information hiding technique, characterized by complete reversibility. The extraction process formula is as follows:

[0195] PackData, NewP = RDH(P)

[0196] EncodeParameters,HmacData=unPack(PackData)

[0197] In the specific reverse process, the pixels are reverse-shifted according to the recorded shift amount to recover the embedded data and histogram, obtain the pixel value distribution of the grayscale image, obtain the grayscale image of the encrypted image, and finally obtain the encrypted image.

[0198] Next, S504 is executed to disassemble the embedded data and obtain the encrypted chaotic parameters and signature data.

[0199] Since the embedded data is a concatenation of encrypted chaotic parameters and signature data, it can be quickly decomposed according to the corresponding positions to obtain the encrypted chaotic parameters and signature data.

[0200] Next, the signature data is verified. Specifically, HMAC-SM3 signature technology is used to verify the signature data in order to determine the security of the image to be decrypted.

[0201] First, obtain the verification data, then compare it with the decomposed signature data to verify the signature. Figure 7 The image shows the signature verification process.

[0202] The process of obtaining the verification data is as follows:

[0203] The AES key obtained in S202 is processed with the encrypted image to obtain verification data. Specifically, the SM3 cryptographic hash algorithm is used to process the encrypted image, generating 256-bit hash data.

[0204] Based on 256-bit hash data, HMAC-SM3 signature is performed using an AES key to obtain verification data.

[0205] The verification data is compared with the decomposed signature data. If they match, the signature verification is successful.

[0206] For unusual issues such as data decryption failure, the data decryption process should be stopped immediately and an alarm should be triggered, and the main body of the center should be notified in a timely manner.

[0207] For normal procedures, continue with the subsequent operations.

[0208] Next, the least significant bit of the encrypted image is obtained, and based on the least significant bit, the location information of the sensitive area of ​​the encrypted image is determined.

[0209] Then, S505 is executed to obtain the chaotic sequence parameters of the specialized two-dimensional Logistic chaotic cipher based on the AES key and the encrypted chaotic parameters.

[0210] By employing the inverse process of AES key encryption, the chaotic sequence parameters, namely X0 and r, of the specialized two-dimensional Logistic chaotic cipher can be recovered.

[0211] The specific pseudocode is as follows:

[0212] iv = EncodeParameters[:16]

[0213] EncodeData= EncodeParameters[16:]

[0214] data=AES.new(kaes, AES.MODE_CBC, iv).decrypt(EncodeData)

[0215] import struct

[0216] x0, r=struct.unpack('dd',data)

[0217] Since the AES key is known, the IV data is also known. The encryption chaotic parameters are filled into a 4×4 byte matrix. The 128-bit AES key is expanded. The matrix is ​​decrypted by the inverse round function in 11 rounds to obtain the decryption result. The decryption result is XORed with the IV data. After unpacking the result, the chaotic sequence parameters of the specialized two-dimensional Logistic chaotic encryptor, namely X0 and r, can be obtained.

[0218] Next, S506 is executed to decrypt the encrypted image based on the chaotic sequence parameters, obtaining the original image, thus realizing the decryption of the encrypted image.

[0219] Specifically, a Logistic chaotic sequence is obtained based on the chaotic sequence parameters and the location information of the sensitive region of the encrypted image;

[0220] Based on the Logistic chaotic sequence, an inverse mapping table is obtained;

[0221] Based on the inverse mapping table, the pixels in the sensitive area are subjected to an inverse XOR operation, and then inversely replaced to obtain the original image.

[0222] First, calculate the Logistic chaotic sequence required for the sensitive region. The specific calculation is determined based on the location information of the sensitive region.

[0223] According to formula X n =r*X n-1 *(1- X n-1 ), where n is determined based on the location information of the sensitive region. This yields the Logistic chaotic sequence, i.e., X0~X... n Next, a specialized Logistic chaotic decryptor is used to decrypt the sensitive areas of the image to obtain the final image.

[0224] like Figure 8 As shown, the decryption process is as follows:

[0225] First, multiply the generated Logistic chaotic sequence corresponding to the current pixel by 255, round it to the nearest integer, and then XOR it with the current pixel to obtain the pixel value of the intermediate pixel. The specific formula is as follows:

[0226] R k =E k ^round(X k *255)

[0227] Among them, X k E represents the value of a Logistic chaotic sequence. k R is the pixel value of the current pixel. k This is the pixel value of the middle pixel.

[0228] Next, the pixel values ​​of the intermediate pixels are reverse-scrambled according to the inverse mapping table, thereby restoring the original image. Finally, the image to be decrypted is decrypted.

[0229] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0230] This invention provides a method for decrypting vehicle network data, comprising: acquiring an image to be decrypted; upon receiving recovery requests initiated by various entities, determining an AES key based on the recovery requests from each entity using the Shamir key recovery method, wherein the recovery requests include AES key fragments corresponding to each entity; extracting embedded data and an encrypted image based on the image to be decrypted; disassembling the embedded data to obtain encrypted chaotic parameters and signature data; obtaining chaotic sequence parameters of a specialized two-dimensional Logistic chaotic encryptor based on the AES key and the encrypted chaotic parameters; and decrypting the encrypted image based on the chaotic sequence parameters to obtain the original image, thereby decrypting the image to be decrypted. This method improves the security of vehicle network data by enabling the legitimate recovery and use of data through multi-party collaboration.

[0231] Example 3:

[0232] Based on the same inventive concept, the present invention also provides an encryption device for vehicle network data, such as... Figure 9 As shown, it includes:

[0233] The first acquisition module 901 is used to acquire images related to vehicle network data and determine the sensitive areas of the images;

[0234] The generation module 902 is used to randomly generate the chaotic sequence parameters and AES key of a specialized two-dimensional Logistic chaotic cipher;

[0235] The first obtaining module 903 is used to encrypt the sensitive region based on the chaotic sequence parameters to obtain an encrypted image;

[0236] The second module 904 is used to encrypt the chaotic sequence parameters based on the AES key to obtain encrypted chaotic parameters;

[0237] The third module 905 is used to obtain signature data based on the sensitive area and the AES key;

[0238] The fourth module 906 is used to obtain embedded data based on the encrypted chaotic parameters and the signature data;

[0239] The embedding module 907 is used to embed the embedded data into the encrypted image to achieve encryption of vehicle network data;

[0240] The distribution module 908 is used to fragment the AES key using the Shamir key distribution method and distribute it to the main entities of car manufacturers, traffic management departments and insurance companies.

[0241] In one alternative implementation, the first receiving module 903 is configured to:

[0242] Obtain the location information of the sensitive area and mark it in the least significant bit of the image;

[0243] Based on the location information of the sensitive region, the length of the required chaotic sequence is determined;

[0244] Based on the length, the corresponding Logistic chaotic sequence is obtained;

[0245] Based on the Logistic chaotic sequence, the sensitive region is encrypted to obtain an encrypted image.

[0246] In one alternative implementation, the first receiving module 903 is configured to:

[0247] After sorting the Logistic chaotic sequence, a mapping table is obtained;

[0248] Based on the mapping table, the original pixels of the sensitive area are shuffled to obtain disordered pixels;

[0249] Based on the Logistic chaotic sequence corresponding to the chaotic pixels and the original pixels, pixel values ​​are replaced through XOR operation to obtain an encrypted image.

[0250] In one alternative implementation, the second obtaining module 904 is configured to:

[0251] The chaotic sequence parameters are packaged to obtain 128 bits of packaged data;

[0252] Based on the packaged data, AES encryption is used to obtain encrypted chaotic parameters.

[0253] In one alternative implementation, the third obtaining module 905 is used for:

[0254] The sensitive area is processed using the SM3 cryptographic hash algorithm to generate 256-bit hash data;

[0255] Based on the 256-bit hash data, HMAC-SM3 signature is performed using an AES key to obtain the signature data.

[0256] In one alternative implementation, the fourth obtaining module 905 is used for:

[0257] The encrypted chaotic parameters are concatenated with the signature data to obtain the embedded data.

[0258] Example 4:

[0259] Based on the same inventive concept, the present invention also provides a device for decrypting vehicle network data, such as... Figure 10 As shown, it includes:

[0260] The second acquisition module 1001 is used to acquire the image to be decrypted;

[0261] The recovery module 1002 is used to determine the AES key based on the recovery request initiated by each subject by using the Shamir key recovery method when receiving the recovery request initiated by each subject. The recovery request includes AES key fragments corresponding to each subject.

[0262] Extraction module 1003 is used to extract embedded data and encrypted image based on the image to be decrypted;

[0263] The disassembly module 1004 is used to disassemble the embedded data to obtain encrypted chaotic parameters and signature data;

[0264] The fifth module 1005 is used to obtain the chaotic sequence parameters of a specialized two-dimensional Logistic chaotic encryptor based on the AES key and the encryption chaotic parameters.

[0265] The decryption module 1006 is used to decrypt the encrypted image based on the chaotic sequence parameters to obtain the original image, thereby realizing the decryption of the image to be decrypted.

[0266] In one alternative implementation, it further includes:

[0267] The signature verification module is used to verify the signature data using HMAC-SM3 signature technology in order to determine the security of the image to be decrypted.

[0268] In one alternative implementation, it further includes:

[0269] The determination module is used to obtain the least significant bit of the encrypted image; based on the least significant bit, it determines the location information of the sensitive area of ​​the encrypted image.

[0270] In one alternative implementation, the decryption module 1006 is used for:

[0271] Based on the chaotic sequence parameters and the location information of the sensitive region of the encrypted image, a Logistic chaotic sequence is obtained;

[0272] Based on the aforementioned Logistic chaotic sequence, an inverse mapping table is obtained;

[0273] Based on the inverse mapping table, the pixels in the sensitive area are subjected to an inverse XOR operation and then inversely replaced to obtain the original image.

[0274] Example 5:

[0275] Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for encrypting vehicle network data or the steps of the method for decrypting vehicle network data.

[0276] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0277] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0278] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various inventive aspects, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all features of the single foregoing disclosed embodiment. Therefore, the claims, following the detailed description, are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0279] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0280] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the specific implementation, any of the claimed embodiments can be used in any combination.

[0281] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the vehicle network data encryption device and vehicle network data decryption device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0282] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware.

[0283] The use of words such as first, second, and third does not indicate any order. These words can be interpreted as names.

Claims

1. A method for encrypting vehicle network data, characterized in that, include: Acquire images of vehicle-to-everything (V2X) data and determine the sensitive areas of the images; Randomly generate the chaotic sequence parameters and AES key of a specialized two-dimensional Logistic chaotic cipher; Based on the chaotic sequence parameters, the sensitive region is encrypted to obtain an encrypted image, including: Obtain the location information of the sensitive area and mark it in the least significant bit of the image; Based on the location information of the sensitive region, the length of the required chaotic sequence is determined; Based on the length, the corresponding Logistic chaotic sequence is obtained; Based on the Logistic chaotic sequence, the sensitive region is encrypted to obtain an encrypted image; Based on the AES key, the chaotic sequence parameters are encrypted to obtain encrypted chaotic parameters; Based on the sensitive area and the AES key, the signature data is obtained; Based on the encrypted chaotic parameters and the signature data, the embedded data is obtained; The embedded data is embedded into the encrypted image to achieve encryption of vehicle network data; The AES key is fragmented using the Shamir key distribution method and distributed to the main entities, including car manufacturers, traffic management departments, and insurance companies.

2. The method as described in claim 1, characterized in that, Based on the Logistic chaotic sequence, the sensitive region is encrypted to obtain an encrypted image, including: After sorting the Logistic chaotic sequence, a mapping table is obtained; Based on the mapping table, the original pixels of the sensitive area are shuffled to obtain disordered pixels; Based on the Logistic chaotic sequence corresponding to the chaotic pixels and the original pixels, pixel values ​​are replaced through XOR operation to obtain an encrypted image.

3. The method as described in claim 1, characterized in that, Based on the AES key, the chaotic sequence parameters are encrypted to obtain encrypted chaotic parameters, including: The chaotic sequence parameters are packaged to obtain 128 bits of packaged data; Based on the packaged data, the encrypted chaotic parameters are obtained by encrypting it with an AES key.

4. The method as described in claim 1, characterized in that, Based on the sensitive area and the AES key, signature data is obtained, including: The sensitive area is processed using the SM3 cryptographic hash algorithm to generate 256-bit hash data; Based on the 256-bit hash data, HMAC-SM3 signature is performed using an AES key to obtain the signature data.

5. The method as described in claim 1, characterized in that, Based on the encrypted chaotic parameters and the signature data, embedded data is obtained, including: The encrypted chaotic parameters are concatenated with the signature data to obtain the embedded data.

6. A method for decrypting vehicle network data, characterized in that, include: Obtain the image to be decrypted; Upon receiving recovery requests initiated by each entity, the AES key is determined based on the recovery requests from each entity using the Shamir key recovery method. The recovery requests include AES key fragments corresponding to each entity. Based on the image to be decrypted, extract the embedded data and the encrypted image; Disassemble the embedded data to obtain encrypted chaotic parameters and signature data; Obtain the least significant bit of the encrypted image; Based on the least significant bit, the location information of the sensitive area of ​​the encrypted image is determined; Based on the AES key and the encryption chaos parameters, the chaos sequence parameters of the specialized two-dimensional Logistic chaotic encryptor are obtained; Based on the chaotic sequence parameters, the encrypted image is decrypted to obtain the original image, thus realizing the decryption of the image to be decrypted.

7. The method as described in claim 6, characterized in that, After extracting the embedded data using the inverse process of the histogram-shifting RDH data extraction algorithm based on the image to be decrypted, the method further includes: The signature data is verified using HMAC-SM3 signature technology to determine the security of the image to be decrypted.

8. The method as described in claim 6, characterized in that, Based on the chaotic sequence parameters, the encrypted image is decrypted to obtain the original image, including: Based on the chaotic sequence parameters and the location information of the sensitive region of the encrypted image, a Logistic chaotic sequence is obtained; Based on the aforementioned Logistic chaotic sequence, an inverse mapping table is obtained; Based on the inverse mapping table, the pixels in the sensitive area are subjected to an inverse XOR operation and then inversely replaced to obtain the original image.

Images