Vehicle Data Encryption Method and Device in the Internet of Vehicles Environment

Abstract

The present invention discloses a vehicle data encryption method and device in a vehicle networking environment, relating to the technical field of data communication. The method includes: for the user side, dividing users according to attribute characteristics to generate a user list; mining the data risk characteristics of the vehicle networking data network, where there are fog computing nodes based on encryption patterns in the vehicle networking data network; initializing and configuring an authorization proxy unit in the form of constructing an alias certificate number with a double hash chain; performing data pattern encryption to generate a first-layer encryption result; conducting a risk control assessment on the first-layer encryption result, and performing a second-layer differential relaxation transformation encryption for sensitive data to determine the data encryption result. It solves the technical problem that existing vehicle data encryption is difficult to adapt to the large amount of data, strong dynamics, and diverse sources in the vehicle networking environment, which in turn leads to the inability to meet the high security and reliability requirements of vehicle data in the vehicle networking environment, and achieves the technical effect of improving the security and reliability of vehicle data encryption.

Description

Technical Field

[0001] This application relates to the technical field of data communication, and particularly to a method and device for encrypting vehicle data in a vehicle networking environment. Background Art

[0002] In the current wave of digital transformation, vehicle networking, as an important part of the intelligent transportation system, is developing at an unprecedented speed. By closely connecting vehicles with the Internet, other vehicles, and infrastructure, it has achieved efficient transmission and sharing of information, greatly improving traffic efficiency. However, with the wide application of vehicle networking technology, the rapid growth and complex interaction of vehicle data have brought unprecedented data security challenges. Vehicle data in a vehicle networking environment covers sensitive information such as vehicle location, driving trajectory, speed, and driving habits. If leaked or illegally used, it will pose a serious threat to personal privacy, corporate interests, etc. How to effectively encrypt and protect vehicle data while ensuring the efficient circulation of data has become a key problem that urgently needs to be solved in the development of vehicle networking technology. Traditional data encryption methods often focus on improving the encryption strength in a single dimension and are difficult to adapt to the characteristics of large data volume, strong dynamics, and diverse sources in a vehicle networking environment. Moreover, traditional encryption mechanisms often rely on a centralized trust model, which has problems such as single-point failure and trust bottlenecks and is difficult to meet the requirements of high availability, high security, and scalability in vehicle networking.

[0003] Therefore, in the current vehicle data encryption related technologies, there are technical problems that it is difficult to adapt to the large data volume, strong dynamics, and diverse sources in a vehicle networking environment, thus resulting in the inability to meet the high security and reliability requirements of vehicle data in vehicle networking. Summary of the Invention

[0004] This application provides a method and device for encrypting vehicle data in a vehicle networking environment, solving the technical problems existing in the current vehicle data encryption that it is difficult to adapt to the large data volume, strong dynamics, and diverse sources in a vehicle networking environment, thus resulting in the inability to meet the high security and reliability requirements of vehicle data in vehicle networking, and achieving the technical effect of improving the security and reliability of vehicle data encryption.

[0005] The present application provides a vehicle data encryption method in a vehicle networking environment. The method includes: for the user side, dividing users according to attribute characteristics to generate a user list, where encryption patterns are assigned to each attribute class in the user list; mining the data risk characteristics of the vehicle networking data network, where there are fog computing nodes based on the encryption patterns in the vehicle networking data network; initializing and configuring an authorization proxy unit by constructing an alias certificate number with a double hash chain, combining the user list and the data risk characteristics, where the authorization proxy unit meets the trusted standard and is not a third party, and the authorization and the certificate do not have a one-to-one relationship; generating vehicle data, updating the alias certificate number in combination with the authorization proxy unit, and performing data pattern encryption based on user attributes to generate a first-level encryption result; performing risk control evaluation on the first-level encryption result, and performing second-level differential relaxation conversion encryption for sensitive data to determine the data encryption result.

[0006] In a possible implementation manner, there are fog computing nodes based on the encryption patterns in the vehicle networking data network, and the following processing is also performed: traversing the user list to determine the data processing mode based on the encryption patterns; determining the fog computing node specifications based on the data processing mode; traversing the vehicle networking data network to determine the high-load positions of data processing and locate the computing extension nodes; extending the fog computing nodes in the vehicle networking data network based on the data processing mode - fog computing node specifications - computing extension nodes, where the fog computing nodes are used for processing task sharing.

[0007] In a possible implementation manner, for mining the data risk characteristics of the vehicle networking data network, the following processing is performed: establishing a vehicle networking data network of a vehicle terminal, a communication network, and a cloud platform; calling the vehicle networking data records in a preset time zone to mine risk confrontation points, where each risk confrontation point is marked with a risk level and a risk type; clustering the risk confrontation points to determine the data risk characteristics and marking the vehicle networking data network.

[0008] In a possible implementation manner, when constructing an alias certificate number with a double hash chain, the following processing is also performed: receiving the authorization information from the regional authorization agency and generating a first certificate number; identifying the authorization time limit of the authorization information and generating a certificate update permission flag; performing certificate invalidation and update management based on the first certificate number and the certificate update permission flag.

[0009] In a possible implementation manner, for performing certificate invalidation and update management, the following processing is also performed: selecting two security seeds, combining with a hash function and determining the starting point of the link to construct a first hash chain and a second hash chain; receiving a certificate update instruction, and determining the first hash value and the second hash value at the target position based on the first hash chain and the second hash chain; splicing the first hash value and the second hash value to generate an updated certificate number; after generating the updated certificate number, combining with the hash function to update the first hash chain and the second hash chain.

[0010] In a possible implementation, two-layer differential relaxation conversion encryption for sensitive data is performed, and the following processing is also performed: traverse the sensitive data, balance the data value and privacy level, determine the differential relaxation degree, and set the differential method; based on the differential method, perform differential conversion processing on the sensitive data to determine the data encryption result.

[0011] In a possible implementation, after determining the data encryption result, the following processing is also performed: receive and merge new vehicle data, identify the sensitive data part and perform processing based on the differential method to determine the merged encrypted data; perform differential invalidation evaluation on the merged encrypted data to generate a differential update instruction; based on the differential update instruction, perform updated differential conversion on the merged sensitive data.

[0012] The present application also provides a vehicle data encryption device in a vehicle networking environment, including: a user list generation module, configured to divide users according to attribute characteristics for the user side and generate a user list, where encryption patterns are assigned to each attribute class in the user list; a data risk feature mining module, configured to mine data risk features of the vehicle networking data network, where there are fog computing nodes based on the encryption pattern in the vehicle networking data network; an authorized proxy unit configuration module, configured to initialize and configure the authorized proxy unit by constructing an alias certificate number in a double hash chain manner, combining the user list and the data risk features, where the authorized proxy unit meets the trusted standard and is not a third party, and the authorization and the certificate do not have a one-to-one relationship; a first-layer encryption result generation module, configured to generate vehicle data, update the alias certificate number in combination with the authorized proxy unit, and perform data pattern encryption based on user attributes to generate a first-layer encryption result; a data encryption result determination module, configured to perform risk control evaluation on the first-layer encryption result, perform two-layer differential relaxation conversion encryption for sensitive data, and determine the data encryption result.

[0013] It is intended to propose a vehicle data encryption method and device in a vehicle networking environment through the present application, divide users according to attribute characteristics for the user side to generate a user list; mine data risk features of the vehicle networking data network, where there are fog computing nodes based on the encryption pattern in the vehicle networking data network; initialize and configure the authorized proxy unit by constructing an alias certificate number in a double hash chain manner; perform data pattern encryption to generate a first-layer encryption result; perform risk control evaluation on the first-layer encryption result, perform two-layer differential relaxation conversion encryption for sensitive data, and determine the data encryption result. The technical problem that existing vehicle data encryption is difficult to adapt to the large amount of data, strong dynamics, and diverse sources in the vehicle networking environment, resulting in the inability to meet the high security and reliability of vehicle data in the vehicle networking, is solved, and the technical effect of improving the security and reliability of vehicle data encryption is achieved. Description of the Drawings

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the devices according to the embodiments of the present application. It should be understood that the operations above or below do not necessarily need to be executed precisely in order. Instead, according to requirements, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 Schematic flowchart of the vehicle data encryption method in the vehicle networking environment provided by the embodiments of the present application;

[0016] Figure 2 Schematic structural diagram of the vehicle data encryption device in the vehicle networking environment provided by the embodiments of the present application.

[0017] Explanation of reference numerals: User list generation module 10, data risk feature mining module 20, authorized agent unit configuration module 30, first-layer encryption result generation module 40, data encryption result determination module 50. Detailed implementation manners

[0018] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product or server comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0021] The embodiments of the present application provide a vehicle data encryption method in a vehicle networking environment, as Figure 1 shown. The method includes:

[0022] Step S100, for the user side, divide users according to attribute characteristics to generate a user list, where encryption patterns are assigned to each attribute class in the user list.

[0023] Preferably, for different users, they are classified according to their attribute characteristics (such as age, gender, geographical location, driving habits, etc.). Specifically, through a detailed analysis of users, the attribute characteristics of each user are identified and analyzed, such as the user's identity information, behavior patterns, location data, and usage habits, etc., to identify the needs and sensitivities of different user groups in data access and processing. Then, based on the analysis results, users are divided into different categories. For example, users can be grouped according to factors such as security requirements, usage frequency, data sensitivity, etc. Each category can represent a specific user group, facilitating the targeted implementation of security policies. The sorted user information is organized into a user list, in which the attribute characteristics of each user are recorded for subsequent encryption and security management. Among them, assigning an encryption pattern to each attribute class in the user list means that a specific encryption style is assigned to each user attribute class in the user list, that is, different encryption strategies are adopted according to different attribute characteristics. For example, sensitive data (such as personal identity information) may require stronger encryption, while general information (such as vehicle status) may adopt a lighter encryption method. This encryption strategy ensures the security of data during transmission and reduces the risk of being maliciously attacked. By combining user classification with encryption styles, the encryption strategy can be dynamically adjusted according to real-time risk assessment. If the risk level of a certain user increases, a stronger encryption method can be quickly applied to protect data security, providing a flexible and efficient solution for data protection in the vehicle networking environment.

[0024] Step S100 further includes step S110, traversing the user list to determine the data processing mode based on the encryption pattern; step S120, determining the fog computing node specifications based on the data processing mode; step S130, traversing the vehicle networking data network to determine the high-load locations of data processing and locate the computing extension nodes; step S140, extending the fog computing nodes to the vehicle networking data network based on the data processing mode - fog computing node specifications - computing extension nodes, where the fog computing nodes are used for processing task sharing.

[0025] Preferably, traverse the user list (including users of different attribute classes), and determine the data processing mode suitable for the user or user group according to the encryption requirements, encryption patterns, data sensitivity, processing performance requirements, and possible compliance requirements (such as GDPR, HIPAA, etc.) of each user in the list, including selecting appropriate encryption algorithms, key management strategies, data encapsulation and transmission protocols, data storage methods, etc., to ensure that the data meets the standards during transmission and storage. For example, select appropriate encryption algorithms (such as AES, RSA, etc.) and decryption processes according to the sensitivity of the data, and select data transmission protocols suitable for the vehicle networking environment, such as TLS, DTLS, etc., to ensure the security of data transmission, and determine the storage format, encryption method, and access control strategy of the data; then select appropriate fog computing node specifications according to the determined data processing mode. The fog computing node specifications include but are not limited to computing power (the model and quantity of processors such as CPUs, GPUs, etc., and the memory size), storage capacity (the storage space size of the node for storing temporary or long-term data), security features, such as the security hardware of the node (such as a TPM module), etc., to ensure the security of the data on the node.

[0026] Preferably, traverse the vehicle networking data network, collect key information such as the operating status, data transmission volume, and processing task volume of nodes such as vehicles, roadside units (RSUs), and data centers. By analyzing the collected data, identify the locations or areas where the data processing tasks are the most onerous, that is, high-load locations. These locations may be busy traffic sections, large parking lots, near data centers, etc., which need to process a large amount of vehicle data, including location information, driving status, traffic signals, etc. After determining the high-load locations, new fog computing nodes need to be selected or configured as computing extension nodes. These nodes will be deployed near the data sources to process data faster and reduce the latency of data transmission. The selection of computing extension nodes needs to consider multiple factors, such as geographical location, network access ability, computing power, storage capacity, etc.; after determining the data processing mode, fog computing node specifications, and the location of computing extension nodes, start deploying these fog computing extension nodes in the actual network. The deployment process includes steps such as physical installation of the nodes, network configuration, software installation and configuration, etc. By deploying computing extension nodes, the vehicle networking can process data more efficiently, reduce the burden on the central server, and reduce the latency of data transmission. If the fog computing nodes are successfully deployed and connected to the vehicle networking network, they will start to undertake some data processing tasks, which may include real-time data analysis, data aggregation, data filtering, etc. Through the sharing of processing tasks by fog computing nodes, the vehicle networking system can respond to data requests more quickly, improve the overall performance and response speed of the system, and enhance the overall security performance.

[0027] Step S200: Mine the data risk characteristics of the vehicle-connected data network, where there are fog computing nodes based on an encryption pattern.

[0028] Preferably, the vehicle-connected data network is a complex network system that includes a large number of nodes such as vehicles, roadside units (RSUs), and data centers. Mining the data risk characteristics of the vehicle-connected data network means analyzing the data transmitted and stored in the vehicle-connected data network to identify potential security risks and vulnerabilities. These risk characteristics may include data sensitivity, access frequency, transmission path, etc. Specifically, identify which data belongs to sensitive information, such as users' location information, driving habits, vehicle identification codes, etc. Once this data is leaked, it may lead to the infringement of user privacy or an increase in security risks. Monitor the transmission path of data in the network to understand how data is transmitted from vehicles to the cloud or other nodes, and identify potential risk points, such as insecure intermediate nodes or communication channels vulnerable to attacks. Analyze the frequency and pattern of data access to identify abnormal behaviors. For example, if a certain user suddenly accesses sensitive data frequently, this may imply the risk of data theft. Conduct vulnerability assessment to evaluate the security of each node in the network, including software vulnerabilities, unencrypted data transmission, etc., to identify potential weaknesses that may be attacked.

[0029] Preferably, the fog computing nodes based on an encryption pattern refer to that in the vehicle networking environment, the fog computing nodes process data by applying specific encryption modes. These fog computing nodes are usually located closer to the data source and can provide low-latency computing and storage services. By extending the encryption style to these nodes, fast local encryption and processing of data can be achieved, thereby improving the security of data transmission and reducing the risk of data being intercepted or tampered with. Specifically, the fog computing nodes are usually deployed closer to the data source (such as vehicles), can process data locally, reduce latency, and improve real-time response capabilities. Apply specific encryption methods to these nodes, such as end-to-end encryption of data, to ensure that even if the data is intercepted during transmission, it cannot be deciphered. Enhance the adaptability of the system by dynamically adjusting the encryption pattern of the nodes. For example, when detecting abnormal traffic or risks, be able to quickly switch to a stronger encryption method. Moreover, fog computing allows data to be processed dispersedly among multiple nodes, which not only improves the data processing efficiency but also reduces the risk of single-point failures and increases the security of the overall system.

[0030] Step S200 further includes Step S210: Establish the vehicle-connected data network of the vehicle end, communication network, and cloud platform; Step S220: Invoke the vehicle-connected data records in the preset time zone and mine the risk confrontation points, where each risk confrontation point is marked with a risk level and a risk type; Step S230: Cluster the risk confrontation points, determine the data risk characteristics, and mark the vehicle-connected data network.

[0031] Preferably, connect the vehicle side (such as intelligent vehicles, in-vehicle devices, etc.), communication networks (such as cellular networks, Wi-Fi, LoRa, NB-IoT, etc.), and cloud platforms (or called cloud platforms, i.e., cloud data centers) to each other to form a network system capable of real-time transmitting and processing vehicle data, that is, establish a vehicle-connected data network. Among them, the vehicle side is the source of data, responsible for collecting various information of the vehicle, such as location, speed, acceleration, vehicle status (such as fuel quantity, tire pressure), driving behavior, etc.; the communication network is the data transmission channel, ensuring that the data between the vehicle side and the cloud platform can be transmitted safely and quickly; the cloud platform (cloud platform) is the center of data processing and storage, responsible for receiving data from the vehicle side, storing, analyzing, processing, and providing corresponding services (such as navigation, vehicle monitoring, fault warning, etc.); then, according to a preset time interval (such as one day, one week, one month, etc.), call corresponding data records from the vehicle-connected data network, which contain the status information and behavior data of the vehicle at different time points, deeply analyze these data records, and dig out possible risk confrontation points. The risk confrontation point refers to the abnormality or potential threat shown in the data, which may represent potential hazards to vehicle safety, data safety, or network safety. Each risk confrontation point will be marked with a risk level (such as high, medium, low) and a risk type (such as data leakage, network attack, abnormal driving behavior, etc.).

[0032] Preferably, after identifying the risk confrontation points, perform clustering analysis on these points. Clustering analysis is a process of grouping data objects into multiple classes or clusters, so that the objects within the same cluster have high similarity, while the objects in different clusters have large differences. By clustering the risk confrontation points, potential patterns and rules in the data can be discovered, so as to more accurately determine the data risk characteristics. After determining the data risk characteristics, perform corresponding marking on the vehicle-connected data network. These marks can be geographical marks indicating the risk location, labels representing the risk type, or identifiers used to distinguish different risk levels. Through these marks, the risk points in the vehicle-connected data network can be monitored and managed in real time to ensure the security of data and the stability of the network.

[0033] Step S300, in the way of constructing an alias certificate number with a double hash chain, combine the user list with the data risk characteristics to initialize and configure the authorization proxy unit, where the authorization proxy unit meets the trusted standard and is not a third party, and the authorization and the certificate do not have a one-to-one relationship.

[0034] Preferably, a hash chain is a data structure that links a series of data blocks (or blocks) using a hash function. Each data block contains the hash value of the previous data block as part of it. A double hash chain may refer to using two or two sets of different hash functions in this process to construct the chain, in order to enhance security and complexity. Using a double hash chain to construct an alias certificate number means that the certificate number is not generated directly, but is the result of two hash processes (possibly different hash functions). Such a processing method can effectively prevent forgery and tampering. The alias number of the certificate is an identifier used to uniquely identify a certificate, but does not directly expose the actual content or sensitive information of the certificate. The alias certificate number generated by the double hash chain increases the difficulty for attackers to crack and forge the certificate, improving the security of the system.

[0035] Preferably, then, in combination with the user list and data risk characteristics, initialize and configure the authorization proxy unit. Specifically, the authorization proxy unit is a middleware or service responsible for permission management and authorization. It determines whether a user has the right to access specific resources or services based on the user's request and the system's security policy. The authorization proxy unit needs to meet the trusted standard and is not a third party, which means it should be operated and managed by an internally trusted institution or department, rather than relying on an external third-party service provider. In traditional authentication methods, there is often a one-to-one mapping relationship between a certificate and authorization permissions, that is, one certificate corresponds to a fixed set of permissions. The non-one-to-one relationship between authorization and certificates means that one certificate may correspond to multiple different permissions or roles, or multiple certificates may share the same set of permissions, which can more flexibly handle different security requirements and scenarios.

[0036] Preferably, select two or two sets of different hash functions, generate a series of data blocks according to the user list and data risk characteristics, use the selected hash function to perform hash processing on each data block in turn, and use the hash value of the previous data block as part of the next data block to construct a double hash chain and use the end value of the hash chain as the alias certificate number; design the architecture and functions of the authorization proxy unit according to the system's security policy and user needs, use the user list and data risk characteristics as inputs, and assign different permissions and roles to different users through specific algorithms or rules to ensure that the operating environment and data security of the authorization proxy unit meet the trusted standard. Design a flexible permission mapping mechanism in the authorization proxy unit, allowing one certificate to correspond to multiple permissions or roles, or multiple certificates to share the same set of permissions, manage the permission mapping relationship through configuration files or databases, etc., and make dynamic adjustments as needed to improve overall security and flexibility and better meet security requirements in different scenarios.

[0037] Step S300 further includes step S310 of receiving authorization information from a regional authorization agency and generating a first certificate number; step S320 of identifying the authorization time limit of the authorization information and generating a certificate update permission flag; and step S330 of performing certificate invalidation and update management based on the first certificate number and the certificate update permission flag.

[0038] Preferably, receive authorization information about a specific user or entity from a regional authorization agency (which may be a relevant department, industry association, or other institution with legal authorization qualifications), which usually includes key elements such as user authentication, authorization scope, and authorization period. Based on this information, generate a unique certificate number, that is, the first certificate number, which will be used as the sole identifier for user or entity authentication and permission management in the system. After generating the first certificate number, further analyze the authorization time limit field in the authorization information to determine the validity period of the certificate. The validity period is the time period during which the certificate remains valid and can be used for authentication and permission management. Based on the identification result of the authorization time limit, generate a certificate update permission flag, which is used to indicate whether the certificate can be updated when it is about to expire or has already expired. It may be a simple boolean value (allowed / not allowed), or a complex data structure containing update conditions and time windows. Perform certificate invalidation and update operations according to the first certificate number and the certificate update permission flag. If the validity period of the certificate has passed, or the certificate needs to be invalidated for other reasons (such as the user's permission being revoked), mark the certificate as invalid and delete or archive it from the system, and the user will not be able to use the certificate for authentication or permission management. If the certificate is still within the validity period but is about to expire, and the certificate update permission flag indicates that an update is allowed, a new certificate number will be generated according to the predetermined update process, and the user's certificate information will be updated. This new certificate will inherit some or all of the permissions of the original certificate and have a new validity period, ensuring the validity and security of the certificate, and at the same time providing users with flexible certificate management services.

[0039] Step S330 further includes step S331 of selecting two security seeds, combining them with a hash function and determining the link starting point to construct a first hash chain and a second hash chain; step S332 of receiving a certificate update instruction and determining the first hash value and the second hash value at the target location based on the first hash chain and the second hash chain; step S333 of concatenating the first hash value and the second hash value to generate an updated certificate number; and step S334 of updating the first hash chain and the second hash chain in combination with the hash function after generating the updated certificate number.

[0040] Preferably, the security and traceability of the certificate number are ensured by combining two security seeds, a hash function, and a certificate update instruction. Specifically, two security seeds are selected. A security seed is a set of randomly generated or predefined data used to increase the complexity and security of the hash chain. It can be a random number, a timestamp, or the hash value of a user ID. Then, a hash function is selected and combined based on its security, collision resistance, and computational efficiency to determine the starting point of the link. The hash function is used to generate each hash value in the hash chain. Taking the two security seeds as inputs, the starting point of the link, which is the first hash value of the hash chain, is obtained after being processed by the hash function. Using the same hash function and security seeds, but possibly processing them in different orders or ways, two independent hash chains are constructed, which are respectively used to generate and verify different parts or stages of the certificate number. When the certificate number needs to be updated (for example, when the certificate is about to expire or the user's permissions change), a certificate update instruction is received. According to the requirements in the update instruction (such as a new timestamp, permission level, etc.), the corresponding target positions are found in the first hash chain and the second hash chain. The target positions may be determined based on a certain rule (such as a time interval, the number of permission changes, etc.). Then, the first hash value and the second hash value at the target positions are respectively extracted from these two chains.

[0041] Preferably, the first hash value and the second hash value extracted from the first hash chain and the second hash chain are concatenated, such as simple string concatenation. The result of the concatenation will be used as part or all of the new certificate number. Other information (such as user ID, timestamp, permission identifier, etc.) may also be combined with the hash value to generate a complete updated certificate number. After generating the updated certificate number, by using the new information (such as a new timestamp, permission change record, etc.) as input and processing it through the hash function and adding it to the end of the chain, the first hash chain and the second hash chain are updated to ensure that they can continue to be used in the subsequent certificate number generation and verification processes. When updating the hash chain, it is necessary to ensure that the continuity of the chain is not disrupted, that is, the new hash value should be generated based on the previous hash value and can be traced back to the starting point of the chain through the hash chain. In this way, even if the certificate number is tampered with or forged, the abnormality can be detected by verifying the integrity of the hash chain.

[0042] Step S400: Generate vehicle data, update the alias certificate number in combination with the authorized agent unit, and perform data pattern encryption based on user attributes to generate a first-level encryption result.

[0043] Preferably, various information is collected from on-vehicle devices or vehicle electronic control units (ECUs) through hardware devices such as sensors and actuators and transmitted to a data processing center through an in-vehicle network, such as speed, position, fuel consumption, engine status, etc., which are vehicle data and may include real-time data and historical data for vehicle monitoring, fault diagnosis, performance optimization, etc. Then, the alias certificate number is updated in combination with the authorized agent unit. Specifically, when a certificate needs to be updated (such as in the case of certificate expiration, user permission change, etc.), the authorized agent unit generates a new alias certificate number according to the new user attributes, permission information or other relevant rules. For example, by interacting with a certificate authority (CA) to obtain a new certificate signature or key, and performing data pattern encryption based on user attributes, that is, selecting a suitable encryption scheme according to the user's attributes. For example, for different types of vehicle data (such as sensitive information and non-sensitive information), different encryption algorithms and key lengths are used, and the access permission to the encrypted data is restricted according to the user's permission level. Among them, user attributes refer to characteristic information related to user identity, permission, role, etc. In the Internet of Vehicles, user attributes may include the user's vehicle type, usage scenario, service requirements, etc.; data pattern encryption is a method of customizing an encryption scheme according to the content and structure of data, not just simply encrypting the data, but designing a suitable encryption algorithm, key management strategy and encryption mode according to the characteristics of the data and the user's needs; finally, after the data pattern encryption process based on user attributes, the vehicle data will be converted into a layer of encrypted result, which can only be decrypted and accessed by users with the corresponding permissions and keys, ensuring the security, integrity and availability of vehicle data.

[0044] Step S500, perform a risk control assessment on the first-layer encrypted result, execute a second-layer differential relaxation transformation encryption for sensitive data, and determine the data encryption result.

[0045] Preferably, in the field of data encryption, risk control assessment is a key link to ensure the effectiveness of encryption strategies and data protection measures. Specifically, for the first-layer encryption result, the risk control assessment mainly includes encryption strength assessment, evaluating whether the algorithms, key lengths, etc. used in the first-layer encryption are secure enough to withstand possible attack methods currently and in a period of time in the future; data integrity verification, checking whether the data remains intact during the encryption process without being tampered with or damaged; compliance inspection, ensuring that the encryption operation complies with relevant requirements, such as the regulations on personal data protection like GDPR; performance impact analysis, evaluating the impact of encryption on performance, including the computing resources and time costs required for the encryption and decryption processes; and then performing second-layer differential relaxation transformation encryption on sensitive data. Differential privacy is a technology that protects personal privacy by adding noise to data or query results, and relaxation transformation may refer to introducing a certain degree of flexibility or adjustment during the encryption process to adapt to different encryption requirements and security requirements. That is, on the basis of the first-layer encryption, differential privacy technology is further used to process sensitive data, and a certain form of relaxation mechanism may be combined to optimize the encryption effect. Specifically, in the data encrypted at the first layer, sensitive data fields or records that need special protection are identified, and differential privacy technology is applied to these sensitive data. By adding an appropriate amount of noise to mask the sensitive information in the original data, it is ensured that both privacy can be protected and the availability of the data will not be overly affected. Then, on the basis of the differential privacy processing, a relaxation mechanism is introduced according to actual needs, which may include adjusting the noise addition strategy, changing the parameters of the encryption algorithm, etc. to optimize the encryption effect and performance. After the second-layer differential relaxation transformation encryption, the final data encryption result is obtained. This result not only protects the privacy of sensitive data but also meets the requirements of data availability and compliance, further improving data security and reducing the risk of data leakage and privacy infringement.

[0046] Step S500 further includes step S510 of traversing the sensitive data, balancing the data value and privacy level, determining the differential relaxation degree, and setting the differential method; step S520 of performing differential transformation processing on the sensitive data based on the differential method to determine the data encryption result.

[0047] Preferably, traverse the data stored in the database to identify which data is sensitive. Sensitive data usually includes personal information (such as name, address, phone number), financial information, health records, etc. After determining the sensitive data, balance the data value and privacy level. Specifically, consider multiple factors, such as relevant requirements, users' privacy preferences, the purpose and manner of data use, etc., to evaluate the value and privacy level of these sensitive data. The value of the data may be related to its importance for business decisions, market analysis, or user services; while the privacy level reflects the degree of risk that may be caused after data leakage. Ensure that the data value is fully utilized while maximizing the protection of users' privacy. Then, based on the evaluation results of the data value and privacy level, as well as specific application scenarios and security requirements, determine the differential relaxation degree. The differential relaxation degree is to balance the relationship between data privacy protection and data availability by adjusting the amount of noise added to the data. The greater the amount of noise, the higher the degree of privacy protection, but the availability of the data will also be correspondingly reduced. On the contrary, the degree of privacy protection is lower, but the availability of the data increases. For data with lower value but higher privacy level, a higher differential relaxation degree can be selected; while for data with higher value and relatively lower privacy level, the differential relaxation degree can be appropriately reduced.

[0048] Preferably, when setting the differential method, it is necessary to consider the characteristics of the data (such as numerical type, categorical type, etc.), the requirements of the query (such as count query, range query, etc.), and the requirements of the differential relaxation degree. The differential method refers to how to specifically add noise to the data to protect privacy in differential privacy technology. Common differential methods include the Laplace mechanism, the exponential mechanism, etc. By selecting an appropriate differential method, it can be ensured that while protecting privacy, the availability of the data is maintained as much as possible. After determining the differential relaxation degree and the differential method, perform differential transformation processing on the sensitive data. Specifically, it means converting the original data into data protected by differential privacy, that is, adding an appropriate amount of noise to the original data. The result of the differential transformation processing will be an encrypted data set, in which the sensitive data has been effectively protected, while maintaining the availability and compliance of the data, and then ensuring data security and personal privacy.

[0049] Step S500 further includes step S530, receiving and merging new vehicle data, identifying the sensitive data part and performing processing based on the differential method to determine the merged encrypted data; step S540, performing a differential invalidation evaluation on the merged encrypted data to generate a differential update instruction; step S550, based on the differential update instruction, performing an updated differential transformation on the merged sensitive data.

[0050] Preferably, new vehicle data from different sources (such as vehicle manufacturers, vehicle operators, third-party data providers, etc.) is received, which may include basic vehicle information (such as vehicle model, license plate number, VIN code), location information, driving data, maintenance records, etc. These new data are integrated and merged to form a complete data set. During the process of merging data, the sensitive data parts are automatically identified, including personal identity information (such as owner's name, contact information), vehicle location trajectory, driving habits, etc. Then, a set differential method is used to process these sensitive data, that is, appropriate noise is added to the sensitive data or other forms of transformation are performed to protect privacy while maintaining the availability of the data as much as possible, forming merged encrypted data, which contains the necessary information for subsequent analysis and reduces the risk of privacy leakage.

[0051] Preferably, then a differential failure assessment is performed on the merged encrypted data, that is, the differential privacy protection effect is checked. Specifically, statistical analysis is performed on the data set to evaluate whether the data after differential processing still retains sufficient privacy protection and whether the availability of the data is not overly affected, and to confirm whether the effect of the differential processing meets the expectations. For example, based on the same differential method, some privacy data can be deduced from the differential data before and after the update. For example, if the vehicle passing data increases by one and the passing number of a certain road checkpoint also increases by one, basic information such as the route can be deduced through the correlation of various data. For some confidential trips, further analysis and processing are required. The differential failure assessment result shows that the differential processing method or parameters need to be adjusted. Finally, a differential update instruction is generated to guide how to perform an updated differential transformation on the existing merged sensitive data to improve the privacy protection effect or optimize the data availability. Finally, according to the differential update instruction, an updated differential transformation is performed on the merged sensitive data, that is, the amount of noise added is adjusted, the differential method is changed, or other relevant parameters are adjusted. The updated data will better balance the relationship between privacy protection and data availability and provide more secure and effective data support for subsequent data analysis and other applications.

[0052] In the above text, reference is made to Figure 1 The vehicle data encryption method in the vehicle networking environment according to the embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe the vehicle data encryption device in the vehicle networking environment according to the embodiment of the present invention.

[0053] A vehicle data encryption device in a vehicle networking environment according to an embodiment of the present invention is used to solve the technical problem that existing vehicle data encryption is difficult to adapt to the large amount of data, strong dynamics, and diverse sources in the vehicle networking environment, which leads to the inability to meet the high security and reliability requirements of vehicle data in the vehicle networking. It achieves the technical effect of improving the security and reliability of vehicle data encryption. The vehicle data encryption device in the vehicle networking environment includes: a user list generation module 10, a data risk feature mining module 20, an authorized proxy unit configuration module 30, a first-layer encryption result generation module 40, and a data encryption result determination module 50.

[0054] The user list generation module 10 is used to divide users according to attribute characteristics for the user side and generate a user list. Among them, each attribute class in the user list is assigned an encryption pattern.

[0055] The data risk feature mining module 20 is used to mine the data risk features of the vehicle networking data. There are fog computing nodes based on the encryption pattern in the vehicle networking data.

[0056] The authorized proxy unit configuration module 30 is used to initialize and configure the authorized proxy unit by constructing an alias certificate number in a double hash chain, combining the user list and the data risk features. Among them, the authorized proxy unit meets the trusted standard and is not a third party, and the authorization and certificate do not have a one-to-one relationship.

[0057] The first-layer encryption result generation module 40 is used to generate vehicle data, update the alias certificate number in combination with the authorized proxy unit, and perform data pattern encryption based on user attributes to generate a first-layer encryption result.

[0058] The data encryption result determination module 50 is used to perform risk control assessment on the first-layer encryption result, perform second-layer differential relaxation conversion encryption for sensitive data, and determine the data encryption result.

[0059] Next, the specific configuration of the user list generation module 10 will be described in detail. The user list generation module 10 further includes: traversing the user list to determine the data processing mode based on the encryption pattern; determining the fog computing node specifications based on the data processing mode; traversing the vehicle networking data to determine the high-load positions of data processing and locate the computing extension nodes; extending the fog computing nodes in the vehicle networking data based on the data processing mode - fog computing node specifications - computing extension nodes. Among them, the fog computing nodes are used for processing task sharing.

[0060] Next, the specific configuration of the data risk feature mining module 20 will be described in detail. The data risk feature mining module 20 may further include: establishing a vehicle connection data network for the vehicle end, communication network, and cloud platform; calling the vehicle connection data records in a preset time zone to mine risk confrontation points, where each risk confrontation point is marked with a risk level and a risk type; clustering the risk confrontation points to determine data risk features and marking the vehicle connection data network.

[0061] Next, the specific configuration of the authorization proxy unit configuration module 30 will be described in detail. The authorization proxy unit configuration module 30 may further include: receiving authorization information from a regional authorization agency and generating a first certificate number; identifying the authorization time limit of the authorization information and generating a certificate update permission flag; performing certificate invalidation and update management based on the first certificate number and the certificate update permission flag.

[0062] Next, the specific configuration of the authorization proxy unit configuration module 30 will be described in further detail. The authorization proxy unit configuration module 30 may further include: selecting two security seeds, combining with a hash function and determining a link starting point to construct a first hash chain and a second hash chain; receiving a certificate update instruction, and based on the first hash chain and the second hash chain, determining the first hash value and the second hash value at the target location; concatenating the first hash value and the second hash value to generate an updated certificate number; after generating the updated certificate number, combining with the hash function to update the first hash chain and the second hash chain.

[0063] Next, the specific configuration of the data encryption result determination module 50 will be described in detail. The data encryption result determination module 50 may further include: traversing the sensitive data, balancing the data value and privacy level, determining the differential relaxation degree, and setting the differential method; based on the differential method, performing differential transformation processing on the sensitive data to determine the data encryption result.

[0064] Next, the specific configuration of the data encryption result determination module 50 will be described in further detail. The data encryption result determination module 50 may further include: receiving and merging new vehicle data, identifying the sensitive data part and performing processing based on the differential method to determine the merged encrypted data; performing differential invalidation evaluation on the merged encrypted data to generate a differential update instruction; based on the differential update instruction, performing updated differential transformation on the merged sensitive data.

[0065] The vehicle data encryption device in the vehicle networking environment provided by the embodiments of the present invention can execute the vehicle data encryption method in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0066] Although this application makes various references to certain modules in the apparatus according to embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and are not used to limit the protection scope of the present invention.

[0067] The above specific implementation manners do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A vehicle data encryption method in a vehicle networking environment, characterized in that: The method comprises: On the user side, users are divided according to their attribute characteristics to generate a user list, wherein each attribute class in the user list is given an encryption pattern; Mining data risk characteristics of vehicle-connected data networks that contain fog computing nodes based on encryption patterns; By constructing an alias certificate number in a double hash chain, the authorization proxy unit is initialized and configured in combination with the user list and the data risk characteristics, wherein the authorization proxy unit meets the trust standard and is not a third party, and the authorization and the certificate are not in a one-to-one relationship; Generate vehicle data, update the alias certificate number in conjunction with the authorization agent unit, and perform data pattern encryption based on user attributes to generate a layer of encryption results; Perform risk control assessment on the first-layer encryption result, execute second-layer differential relaxation transformation encryption for sensitive data, and determine the data encryption result; Among them, the method of constructing an alias certificate number with a double hash chain includes: Receiving authorization information from a regional authorization agency and generating a first certificate number; Identify the authorization time limit of the authorization information and generate a certificate update permission mark; Based on the first certificate number and the certificate update permission mark, perform certificate revocation and update management; The certificate revocation and renewal management includes: Select two security seeds, combine the hash function and determine the starting point of the link, and build the first hash chain and the second hash chain; Receive a certificate update instruction, and determine a first hash value and a second hash value of a target location based on the first hash chain and the second hash chain; Concatenate the first hash value and the second hash value to generate an update certificate number; After generating the update certificate number, the first hash chain and the second hash chain are updated in combination with the hash function; Performs two-layer differential relaxed transform encryption for sensitive data, including: Traverse the sensitive data, balance the data value and privacy, determine the differential relaxation, and set the differential method; Based on the differential method, differential conversion processing is performed on the sensitive data to determine the data encryption result.

2. The vehicle data encryption method in a vehicle networking environment as claimed in claim 1, characterized in that: The vehicle-connected data network has fog computing nodes based on encryption, including: Traversing the user list to determine a data processing mode based on the encryption pattern; Determining fog computing node specifications based on the data processing mode; Traversing the vehicle-connected data network, determining high-load locations for data processing, and locating and calculating extended nodes; Based on the data processing mode-fog computing node specification-computing extension node, the vehicle-connected data network is extended with fog computing nodes, wherein the fog computing nodes are used to share processing tasks.

3. The vehicle data encryption method in a vehicle networking environment as claimed in claim 1, characterized in that: The data risk characteristics of the vehicle-connected data network are mined, including: Establish a vehicle-connected data network for the vehicle side, communication network and gimbal; Call the vehicle data records of the preset time zone to mine risk confrontation points, where each risk confrontation point is marked with risk level and risk type; The risk confrontation points are clustered, data risk characteristics are determined and marked on the connected vehicle data network.

4. The vehicle data encryption method in a vehicle networking environment as claimed in claim 1, characterized in that: After the data encryption result is determined, it includes: Receive and merge the newly added vehicle data, identify the sensitive data part and perform processing based on the differential method, and determine the merged encrypted data; Performing differential failure assessment on the combined encrypted data to generate differential update instructions; Based on the differential update instruction, the merged sensitive data is updated with a differential conversion.

5. A vehicle data encryption device in a vehicle networking environment, characterized in that: The device is used to implement the vehicle data encryption method in a vehicle networking environment according to any one of claims 1 to 4, and the device includes: A user list generation module is used to divide users according to attribute characteristics and generate a user list for the user side, wherein each attribute class in the user list is given an encryption pattern; A data risk feature mining module, used to mine data risk features of a vehicle-connected data network, where the vehicle-connected data network has fog computing nodes based on encryption patterns; An authorization proxy unit configuration module is used to initialize and configure an authorization proxy unit by building an alias certificate number in a double hash chain, combining the user list and the data risk characteristics, wherein the authorization proxy unit meets the trust standard and is not a third party, and the authorization and the certificate are not in a one-to-one relationship; A first-layer encryption result generation module, used to generate vehicle data, update the alias certificate number in conjunction with the authorization agent unit, and perform data pattern encryption based on user attributes to generate a first-layer encryption result; The data encryption result determination module is used to perform risk control evaluation on the first-layer encryption result, execute the second-layer differential relaxation transformation encryption for sensitive data, and determine the data encryption result.

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