Vehicle-mounted crowd-sensing data encryption transmission method and system

Abstract

The present application belongs to the technical field of data transmission of vehicle-mounted crowd wisdom perception system, and discloses a kind of vehicle-mounted crowd wisdom perception data encryption transmission method and system.Using the key exchange protocol based on SM2 digital signature, the security of data transmission is ensured, including signature and session key correctness;In the data processing process, the anonymity and traceability of users are realized by generating pseudonyms.The present application solves the problem of user identity privacy and user data security in the vehicle-mounted crowd wisdom perception system.Enhances user privacy protection: by generating pseudonyms for each vehicle user, the privacy of users is protected, while retaining the ability to trace the real identity of users when necessary.Improves the scalability and flexibility of the system: the design of edge node enables the system to flexibly handle tasks of different sizes, and can be screened according to the credit degree of vehicle users, improving the efficiency and quality of task execution.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data transmission, and particularly relates to a data transmission method and system for a vehicle-mounted crowd-sensing system. BACKGROUND

[0002] The significant progress of Internet of Things (IoT) and vehicle technology has jointly promoted the vigorous development of intelligent connected vehicles (ICVs). ICVs, with their rich sensors and advanced wireless communication equipment, not only support mobile crowdsourcing sensing (MCS) applications, but also give birth to an innovative sensing mode, vehicle crowdsourcing sensing (VCS). VCS significantly reduces the cost of data collection, including financial and time costs, by delegating sensing tasks to ICVs with high mobility and strong sensing capabilities, while providing superior service quality compared to traditional MCS. This progress has led to the emergence of a series of VCS applications, such as intelligent parking, traffic monitoring, accident information collection, map updating, intelligent navigation, and road surface monitoring, which are accelerating the construction process of smart cities.

[0003] A typical vehicle-mounted crowd-sensing system architecture includes three core components: participants (i.e., vehicles providing sensing data), a sensing platform (composed of multiple servers located in a data center), and requesters (entities sending requests to the sensing platform to obtain the required sensing data). These three components work together to support the core operation framework of the vehicle-mounted crowd-sensing system.

[0004] Under the close cooperation between entities, through a series of operations such as sensing data collection, uploading, processing, and storage, sensing tasks can be efficiently completed. However, in this process, user privacy security and data transmission security have become key issues that need to be addressed. On the one hand, improper data processing or transmission methods may leak users' identity privacy information, causing users' concerns and affecting their enthusiasm for participating in sensing tasks, which may ultimately lead to a decrease in data quality and quantity. On the other hand, security vulnerabilities in the data transmission process may be exploited by malicious users to upload false data or tamper with real data, thereby seriously affecting the accuracy and reliability of sensing tasks.

[0005] In recent years, some solutions have been proposed to address the issues of user privacy security and data transmission security. For example, the GoSense system proposed by TY et al. focuses on the optimization of vehicle recruitment and task allocation, but in practical applications, it still needs to consider how to strengthen the security protection of user privacy and data transmission while ensuring task efficiency. F. Li et al. try to link task rewards with data quality to encourage users to improve data accuracy, but this solution also needs to be based on solid privacy protection and data transmission security to ensure the enthusiasm of user participation and the authenticity of data. In addition, the malicious user detection scheme designed by Wang et al. introduces blockchain technology to enhance privacy protection, but in the process of ensuring data quality, how to balance the needs of data transparency and user privacy protection is still a major challenge.

[0006] Through the above analysis, the problems and defects of the prior art are:

[0007] (1) Insufficient protection of user privacy security: In the process of collecting, uploading, processing and storing perception data, improper data processing or transmission methods may leak the identity privacy information of users. This not only causes the concern of users, but also may reduce their enthusiasm for participating in perception tasks, thereby affecting the quality and quantity of data.

[0008] (2) Data transmission security has vulnerabilities: Security vulnerabilities in the data transmission process may be exploited by malicious users, resulting in the uploading of false data or the tampering of real data. This not only seriously affects the accuracy and reliability of perception tasks, but also may have a negative impact on the construction and operation of smart cities. The existing data transmission security mechanism is not capable of dealing with complex and variable network attacks, and more advanced and effective technical means are needed to ensure the security of data transmission. SUMMARY

[0009] In view of the problems existing in the prior art, the present application provides a data transmission method and system for a vehicle-mounted crowd-sensing system to solve the problem of data transmission of the vehicle-mounted crowd-sensing system.

[0010] The present application is implemented as follows: a data transmission method for a vehicle-mounted crowd-sensing system, which adopts a key exchange protocol based on SM2 digital signature to ensure the security of data transmission, including signature and session key correctness; generates a pseudonym in the data processing process to realize the anonymity and traceability of users. The system comprises a perception platform, an edge node and a vehicle user.

[0011] The sensing platform (SP) has sufficient storage and computing resources to support the crowdsourcing service. It is mainly responsible for receiving tasks from data demanders and distributing them to edge nodes located in the sensing area. After collecting the task reports of vehicle users, the vehicle users who complete the tasks will be rewarded and their credit will be improved. At the same time, it is also responsible for the registration management of vehicles. In order to protect the privacy of vehicle users, the SP will generate a pseudo-identity for each vehicle user. When it is necessary to track the illegal vehicles, the SP can reveal or disclose the real identity of the vehicle.

[0012] The edge node (EN) distributes the tasks to nearby vehicle users after receiving the tasks distributed by the sensing platform. At the same time, it will predict the credit of the vehicle users and screen out the vehicle users who meet the requirements of the sensing tasks.

[0013] The vehicle user (Smart Vehicular User, V) is a modern vehicle equipped with powerful computing, communication and storage functions, and has high mobility. Vehicle users need to ensure that their devices have sufficient performance to support various functions. They participate in sensing tasks to collect data and perform signature processing, and finally share data through edge nodes to obtain credit or rewards.

[0014] Further, the implementation steps of the method are:

[0015] Step one, system setting, for publishing the parameter list of the system;

[0016] Step two, user registration, for vehicle-mounted users to register their identities, and the sensing platform generates a pseudonym for them to protect their identity privacy, and the real identity of the illegal vehicle can be traced;

[0017] Step three, task distribution, for the sensing platform to send sensing tasks to edge nodes, and once the task instructions are received, the edge nodes will immediately forward them to the adjacent vehicle user group;

[0018] Step four, task receiving, for vehicle users who intend to participate in the task will actively respond to the recruitment of the edge node and upload their user data to the edge node. These users will then start the crowd sensing activity and return the collected data results to the edge node;

[0019] Step five, data transmission, for the edge node to further aggregate the digital signatures of the users who complete the tasks, generate an aggregated signature, and interact with the sensing platform to negotiate and establish a session key. Through this session key, the edge node will securely transmit the sensing data to the sensing platform after encrypting the sensing data;

[0020] Step six, task review, is used to verify the user's signature after the perception platform task receives the result information, and give corresponding rewards to vehicle users who successfully submit valid data.

[0021] Furthermore, the step 1 includes:

[0022] Input security parameter λ, randomly select a large prime number q, and determine the non-singular elliptic curve E:y 2 =x 3 +ax+b(mod q)(where, ), select a prime n-order cyclic group from all points of E (including the point at infinity) and generators Choosing a secure hash function Key derivation function KDF and decompression function uncompress. Public system parameter list

[0023] Furthermore, the second step includes:

[0024] During the user registration phase, the specific process between user V and the perception platform SP is as follows:

[0025]

[0026] The user first randomly selects Then calculate P = d·G. Let the user's private key be sk v =d, user public key is pk v =P. Use the public key pk of the perception platform SP Encrypt the information M, H(M), and pk submitted during registration v and random number a, and send the encrypted result to SP, where M=(ID v ||m * ), ID v is the real identity of the user, m * For the user's personal information data, H(M) ensures the integrity of the user's registration information.

[0027] SP→V:E a [ID v ']

[0028] After receiving the encrypted information, SP uses its private key sk SP Decrypt and get M, H(M), pk v And random number a, verify the integrity of M through H(M). After verification, SP generates a pseudonym ID for the user v ', calculate ID v '=H(ID v), and send it to the user after encryption with a, and the user decrypts it with a to get ID v , and the user uses the pseudonym to communicate with others. When the user is investigated for responsibility, the SP shows or exposes the real identity of the user.

[0029] Further, the step three includes:

[0030] The task distribution link is a key step for the interaction between the perception platform SP, the edge node EN and the user V. The specific process is as follows:

[0031] σ Task = Sign(sk SP , Task)

[0032] The SP first creates the task information Task = (work || re || cr th || T1), wherein work is the task details, re is the reward obtained by completing the task, cr th is the credit threshold required by the task for the user credit, and T1 is the current timestamp. Then, the SP calculates H(Task) and signs the task using the private key sk SP to generate σ Task .

[0033] Verify(pk SP , σ Task )

[0034] The SP distributes the task information with the signature to the nearby EN. After receiving the information, the EN calculates H(Task) to verify the integrity of the task information, and uses pk SP to verify the signature to ensure the authenticity of the task information. If the verification is successful, the EN forwards the task information to the surrounding users; otherwise, the EN will discard the task information to prevent the spread of errors. After receiving the task information, the user also verifies the integrity and authenticity. After verification, the user decides whether to participate in the task according to the requirements of the task and the conditions of the user.

[0035] Further, the step four includes:

[0036] The task receiving is the preliminary stage for the user to participate in the perception task, and the specific process is as follows:

[0037]

[0038] According to the task requirements, assume that a certain user who is willing to participate is the γth user (1≤γ≤n), and the EN verifies the task information to ensure the integrity and authenticity of the task information. If the verification is passed, the user randomly selects a random number and calculates K γ= k γ G = (x γ ,y γ ), e γ = H(w γ ), r γ = (e γ +x γ )(mod n). w γ is the request information of the γth user participating in the perception task, i.e. wherein is the pseudonym of the γth user, m γ * is the personal information data of the γth user, T2 is the current timestamp, and b is a random number. If r γ = 0 or r γ +k γ = 0, then k γ is reselected and recalculation is performed; otherwise, s γ = (1+d γ ) -1 (k γ -r γ d γ )(mod n) is calculated. If s γ = 0, then a random number is reselected and returned, otherwise a signature σ γ = (r γ ,s γ ) = Sign(d γ ,w γ ) is generated. The user then encrypts σ EN and w γ using pk γ , and sends the encrypted results to the EN.

[0039] After receiving the encrypted information, the EN decrypts the information using its private key to obtain σ γ and w γ . First, it is determined whether the time interval ΔT = T2-T1 is within the threshold range. If the threshold is exceeded, the data is discarded. If the threshold is within the threshold, the EN checks whether the following equation is established: r ≠ H(w γ ). If the equation is not established, the verification fails; otherwise, e' γ = H(w' γ ) is calculated to check the integrity of w' γ . If t γ ≠ 0, then t γ = (r' γ +s' γ )(mod n) is further calculated, K' γ = s' γ G+t γ P γ = (x' γ ,y'γ ), r γ = (e γ + x γ )(mod n). Finally, check whether r γ = r γ holds. If so, confirm that the signature σ γ is valid.

[0040] Further, the step five comprises:

[0041] T E stores the transaction-aware data of EN, T s stores the task reward re of SP. EN calculates the random number k E = (s E (1 + d E ) + r E d E )(mod n) in the signature using the private key, calculates x s = (r s - H(T s ))(mod n), and the transaction public key the common secret calculates and outputs the session key SK E = KDF(x ES ).

[0042] SP calculates the random number k S = (s S (1 + d S ) + r S d S )(mod n) in the signature using the private key, calculates x E = (r E - H(T E ))(mod n), and the transaction public key calculates the common secret calculates and outputs the session key SK S = KDF(x ES ). EN sends the aware task data collected from the user, the user pseudonym group, and the signature group to SP through the session key.

[0043] Further, the step six comprises:

[0044] SP decrypts using the session key to obtain the task result information, first judges whether the time interval ΔT = T2-T1 is within the threshold range, if not within the threshold range, discards the data, otherwise, audits , if the information is verified to be true, starts batch verification of η user signatures (σ1, σ2,..., σ ηThe effectiveness of the signature P is verified by SP verification If not, the verification fails, otherwise, for i = 1, 2,..., η, calculate in turn: Verify t γ ≠ 0, if not, calculate e' γ = H(w γ ) to w γ The integrity of the signature P is verified by calculating Verify If yes, the signature P is a valid signature. SP gives a certain amount of rewards re to all users in the group, and increases the reputation of the user.

[0045] Another object of the present application is to provide a vehicle-mounted crowd-sensing data encryption transmission system, comprising:

[0046] System setting module: for publishing the parameter list of the system;

[0047] User registration module: for vehicle-mounted user identity registration, the sensing platform generates a pseudonym for the user to protect the user's identity privacy, and the real identity of the vehicle user can be traced for violation;

[0048] Task distribution module: for the sensing platform to send sensing tasks to the edge node, and once the task instruction is received, the edge node immediately forwards it to the adjacent vehicle user group;

[0049] Task receiving module: for vehicle users who intend to participate in the task to actively respond to the recruitment of the edge node, and upload their user data to the edge node, and then start the crowd-sensing activity and return the collected data results to the edge node;

[0050] Data transmission module: for the edge node to further aggregate the digital signature of the user who completes the task, generate an aggregated signature, and interact with the sensing platform to negotiate and establish a session key. Through the session key, the edge node encrypts the sensing data and securely transmits it to the sensing platform;

[0051] Task auditing module, for the sensing platform to verify the user signature after receiving the result information, and give corresponding rewards to the vehicle users who successfully submit valid data.

[0052] The present application solves the problems of the prior art:

[0053] Insufficient protection of user privacy and security: during the collection, uploading, processing and storage of sensing data, improper data processing or transmission methods may leak the user's identity privacy information. This not only causes the user's concern, but also may reduce the user's enthusiasm for participating in the sensing task, thereby affecting the quality and quantity of the data. ​

[0054] Data transmission security vulnerabilities: Security vulnerabilities in the data transmission process can be exploited by malicious users, leading to the uploading of false data or the tampering of real data. This not only seriously affects the accuracy and reliability of perception tasks, but also may have a negative impact on the construction and operation of smart cities. Existing data transmission security mechanisms are not sufficient in dealing with complex and variable network attacks, and more advanced and effective technical means are needed to ensure the security of data transmission.

[0055] In combination with the above technical solutions and the technical problems solved, the technical solution to be protected by the present application has the following advantages and positive effects:

[0056] Firstly, the present application proposes a vehicle-mounted group wisdom perception data encryption transmission method and system to address the problems existing in the prior art, such as insufficient user privacy security protection and data transmission security vulnerabilities. The significant technical progress of the present application is that by using a key exchange protocol based on SM2 digital signature, the security of data transmission is enhanced, ensuring the confidentiality and integrity of data during transmission. At the same time, by generating pseudonyms, the anonymity of users is achieved, which not only protects the privacy of users but also ensures that the real identity of users can be traced back when necessary, effectively balancing privacy protection and regulatory needs.

[0057] Secondly, from the perspective of product, the technical solution to be protected by the present application has the following technical effects and advantages:

[0058] (1) Improved data transmission security: By using SM2 digital signature, the security of data during transmission is ensured, preventing data from being tampered with or stolen.

[0059] (2) Enhanced user privacy protection: By generating pseudonyms for each vehicle user, the privacy of users is protected, while retaining the ability to trace the real identity of users when necessary.

[0060] (3) Improved system scalability and flexibility: The design of edge nodes enables the system to flexibly handle tasks of different scales and can filter based on the credit of vehicle users, improving the efficiency and quality of task execution.

[0061] (4) Promote the participation of vehicle users: Through the incentive mechanism, vehicle users are encouraged to actively participate in data collection and sharing, improving the activity level of the group wisdom perception system and the richness of data.

[0062] Third, in the field of data transmission technology, the traditional method often cannot effectively balance the security of data and the privacy protection needs of users. The application not only improves the security of data transmission, but also realizes the effective protection of user privacy, providing a new possibility for the development of vehicle group intelligent sensing system. In addition, the application improves the participation of vehicle users and the overall efficiency of the system through the reward mechanism and credit management, providing strong technical support for the application and development of vehicle group intelligent sensing system.

[0063] To solve the above problems, the application provides a vehicle group intelligent sensing data encryption transmission method. The method adopts a key exchange protocol based on SM2 digital signature to ensure the security of data transmission, including signature and session key correctness; and generates a pseudonym in the data processing process to realize the anonymity and traceability of users. The application solves the problems of user identity privacy and user data security in the vehicle group intelligent sensing system.

[0064] The implementation of the method includes system setting, user registration, key generation, task distribution and credit prediction steps. First, the parameter list of the public system is used. Then, the vehicle user registers his identity, and the sensing platform generates a pseudonym for him to protect the user's identity privacy and trace the real identity of the illegal vehicle. The sensing platform sends a sensing task to the edge node, and once the task instruction is received, the edge node immediately forwards it to the adjacent vehicle user group. The vehicle user who intends to participate in the task will actively respond to the recruitment of the edge node and upload his user data to the edge node. These users then start the group intelligent sensing activity and return the collected data results to the edge node. The edge node further aggregates the digital signatures of the users who complete the task to generate an aggregated signature, and interacts with the sensing platform to negotiate and establish a session key. Through the session key, the edge node encrypts the sensing data and securely transmits it to the sensing platform. Finally, the sensing platform verifies the user's signature after receiving the result information of the task, and gives appropriate rewards to the vehicle users who successfully submit valid data. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 is a vehicle group intelligent sensing data encryption transmission method flowchart provided by an embodiment of the application;

[0066] Figure 2 is a typical vehicle group intelligent sensing system schematic diagram provided by an embodiment of the application;

[0067] Figure 3 is a running time comparison diagram provided by an embodiment of the application.

[0068] Figure 4 is a vehicle group intelligent sensing data encryption transmission system structure block diagram provided by an embodiment of the application. DETAILED DESCRIPTION

[0069] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0070] As shown in Figure 1 , the vehicle-mounted crowd-sensing data encryption transmission method provided by the embodiment of the present application includes the following steps:

[0071] S101, system setting, for parameter list of public system;

[0072] S102, user registration, for identity registration of vehicle-mounted user, the sensing platform generates a pseudonym for it to protect the user's identity privacy, and can trace the real identity of the illegal vehicle;

[0073] S103, task distribution, for the sensing platform to send sensing tasks to edge nodes, once the task instruction is received, the edge node immediately forwards it to the adjacent vehicle user group;

[0074] S104, task receiving, for vehicle users who intend to participate in the task will actively respond to the recruitment of the edge node, and upload their user data to the edge node, these users will then start the crowd-sensing activity, and return the collected data results to the edge node;

[0075] S105, data transmission, for the edge node to further aggregate the digital signature of the user who completes the task, generate an aggregated signature, and interact with the sensing platform to negotiate and establish a session key. Through the session key, the edge node will safely transmit the sensing data to the sensing platform after encryption;

[0076] S106, task auditing, for the sensing platform to verify the user signature after receiving the result information, and give corresponding rewards to the vehicle users who successfully submit valid data.

[0077] As shown in Figure 2 , the vehicle-mounted crowd-sensing data encryption transmission method provided by the embodiment of the present application includes the following steps:

[0078] (1) In the sensing platform (SP), the session key generation and distribution are performed based on the SM2 digital signature key exchange protocol, to ensure the security of the data in the transmission process, wherein the session key contains signature and encryption verification functions to ensure the integrity and correctness of the data;

[0079] (2) In the perception platform, a pseudo identity is generated for each vehicle user to achieve user anonymity, while retaining the real identity information to achieve traceability, and the real identity of the vehicle is revealed or disclosed when necessary to track the illegal vehicle;

[0080] (3) The edge node (EN) receives the perception tasks from the perception platform, screens them based on the credit prediction of the vehicle users, and distributes the tasks to the vehicle users who meet the requirements;

[0081] (4) The vehicle user (V) receives the task request from the edge node, collects and processes the data, encrypts the data by generating a digital signature, and transmits the encrypted data to the edge node. The vehicle user obtains corresponding credits and rewards by completing the sensing task;

[0082] (5) After collecting the task data uploaded by the vehicle user, the perception platform decrypts and verifies the integrity and authenticity of the data, rewards the vehicle user who completes the task and improves his credit.

[0083] S101 provided in this embodiment of the present invention includes:

[0084] Input security parameter λ, randomly select a large prime number q, and determine the non-singular elliptic curve E:y 2 =x 3 +ax+b(mod q)(where, ), select a prime n-order cyclic group from all points of E (including the point at infinity) and generators Choosing a secure hash function Key derivation function KDF and decompression function uncompress. Public system parameter list

[0085] S102 provided in this embodiment of the present invention includes:

[0086] During the user registration phase, the specific process between user V and the perception platform SP is as follows:

[0087]

[0088] The user first randomly selects Then calculate P = d·G. Let the user's private key be sk v =d, user public key is pk v =P. Use the public key pk of the perception platform SP Encrypt the information M, H(M), and pk submitted during registration v and random number a, and send the encrypted result to SP, where M=(ID v ||m * ), IDv M is the real identity of the user * H(M) guarantees the integrity of the user's registration information.

[0089] SP->V:E a [ID v '

[0090] After receiving the encrypted information, the SP decrypts it using its private key sk SP to obtain M, H(M), pk v and a random number a, and verifies the integrity of M through H(M). After verification, the SP generates a pseudonym ID v ' for the user, calculates ID v ' = H(ID v ), and sends it to the user after being encrypted with a. After receiving it, the user decrypts it with a to obtain ID v ', and uses it for subsequent communication. When the user is investigated for responsibility, the SP presents or exposes the user's real identity.

[0091] The S103 provided by the embodiment of the application comprises:

[0092] The task distribution link is a key step for interaction between the perception platform SP, the edge node EN and the user V. The specific process is as follows:

[0093] σ Task = Sign(sk SP , Task) SP first creates task information Task = (work || re || cr th || T1), wherein work is task details, re is a reward obtained by completing the task, cr th is a credit threshold required by the task for the user's credit, and T1 is a current timestamp. Then, the SP calculates H(Task) and signs the task using its private key sk SP to generate σ Task .

[0094] Verify(pk SP , σ Task )

[0095] The SP distributes the task information with the signature to the nearby EN. After receiving the information, the EN calculates H(Task) to verify the integrity of the task information, and uses pk SPThe signature is verified to ensure the authenticity of the task information. If verification is successful, the EN forwards the task information to surrounding users; otherwise, the EN discards the task information to prevent the spread of errors. After receiving the task information, the user also verifies its integrity and authenticity. After verification, the user decides whether to participate in the task based on the task requirements and their own conditions.

[0096] S104 provided in the embodiment of the present invention includes:

[0097] Task acceptance is the initial stage of user participation in the perception task. The specific process is as follows:

[0098]

[0099] According to the task requirements, it is assumed that a user who is willing to participate is the γth user (1≤γ≤n), and the task information is verified as EN to ensure the integrity and authenticity of the task information. If the verification is passed, the user randomly selects a random number. And calculate K γ =k γ G=(x γ ,y γ ), e γ =H(w γ ),r γ =(e γ +x γ )(mod n). w γ is the request information of the γth user participating in the perception task, that is, in is the pseudonym of the γth user, m γ * is the personal information data of the γth user, T2 is the current timestamp, and b is a random number. γ =0 or r γ +k γ =0, then reselect k γ Calculate again; otherwise calculate s γ =(1+d γ ) -1 (k γ -r γ d γ )(mod n). If s γ =0, then return to reselect the random number, otherwise generate the signature σ γ =(r γ ,s γ )=Sign(d γ ,w γ ). The user then uses pk EN Encryption γ and w γAnd send the encryption result to EN.

[0100] After EN receives the encrypted information, it decrypts it using its private key to obtain σ γ And w γ First, determine whether the time interval ΔT = T2-T1 is within the threshold range. If it exceeds the threshold, discard the data. If it is within the threshold, EN verifies whether is true. If it is not true, the verification fails. Otherwise, calculate e' γ = H(w' γ ) to verify the integrity of w' γ If t γ ≠ 0, further calculate t γ = (r' γ + s' γ )(mod n), K γ '= s' γ G + t γ P γ = (x' γ , y' γ ), r γ '= (e' γ + x' γ )(mod n). Finally, verify whether r' γ = r γ is true. If it is true, confirm that the signature σ γ is valid.

[0101] The S105 provided by the embodiment of the present application comprises:

[0102] T E stores the transaction awareness data of EN, and T s stores the task reward re of SP. EN calculates the random number k E = (s E (1+d E )+r E d E )(mod n) in the signature using a private key, calculates x s = (r s -H(T s ))(mod n), and transaction public key Common secret calculates and outputs the session key SK E = KDF(x ES ).

[0103] SP calculates the random number k S = (s S (1+d S )+r S dS )(mod n), calculate x E =(r E -H(T E ))(mod n), transaction public key Calculating the common secret Calculate and output the session key SK S =KDF(x ES ). EN sends the sensing task data collected from the user, the user pseudonym group, and the signature group to SP through the session key.

[0104] S106 provided in this embodiment of the present invention includes:

[0105] SP uses the session key to decrypt and obtain the task result information. First, it determines whether the time interval ΔT = T2-T1 is within the threshold range. If it is not within the threshold range, the data is discarded. Otherwise, the data is If the information is verified to be true, batch verification of n user signatures (σ1,σ2,...,σ η ) validity. SP test Is it true? If not, the verification fails. Otherwise, for i=1,2,...,η, calculate in sequence: Test t γ ≠0 if not true, if true then calculate e' γ =H(w γ ) come w γ The integrity of the calculation test Is it established? If so, sign is a valid signature. SP gives a certain amount of reward re to all users in the group and increases their credibility.

[0106] like Figure 4 As shown, the vehicle-mounted crowd intelligence perception data encryption and transmission system provided by the embodiment of the present invention includes:

[0107] System settings module: used to disclose the system parameter list;

[0108] User registration module: used for in-vehicle user identity registration. The sensing platform generates a pseudonym for the user to protect the user's identity privacy and can also track the true identity of the illegal vehicle;

[0109] Task distribution module: used by the perception platform to send perception tasks to edge nodes. Once the task instruction is received, the edge node will forward it to the neighboring vehicle user group.

[0110] Task receiving module: vehicle users who voluntarily participate in tasks will actively respond to the recruitment of edge nodes and upload their user data to the edge nodes, these users will then start the crowd sensing activity and return the collected data results to the edge nodes;

[0111] Data transmission module: for the edge node to further aggregate the digital signature of the user who completes the task, generate an aggregated signature, and interact with the sensing platform to negotiate and establish a session key. Through the session key, the edge node will securely transmit the sensing data to the sensing platform after encryption;

[0112] Task auditing module, for the sensing platform task to receive result information to verify the user signature, and give corresponding rewards to the vehicle users who successfully submit valid data.

[0113] Embodiment one: data transmission method of vehicle-mounted crowd sensing system

[0114] The present application provides a kind of data transmission method of vehicle-mounted crowd sensing system, its special feature is, the method adopts the key exchange protocol based on SM2 digital signature, ensure the security of data transmission, including signature and session key correctness;Pseudonym is generated in data processing process to realize the anonymity and traceability of user.The system includes: sensing platform, edge node and vehicle user.

[0115] The sensing platform (Sensing Platform, SP) has sufficient storage and computing resources to support crowdsourcing services. It is mainly responsible for receiving tasks from data demanders and distributing them to edge nodes located in the sensing area. After collecting the task reports of vehicle users, it will reward the vehicle users who complete the task and improve their credit. It is also responsible for the registration and management of vehicles. In order to protect the privacy of vehicle users, SP will generate a pseudo-identity for each vehicle user. When it is necessary to track the illegal vehicles, SP can reveal or disclose the real identity of the vehicle.

[0116] The edge node (Edge Node, EN) receives the tasks distributed by the sensing platform and distributes them to nearby vehicle users. At the same time, it will predict the credit of vehicle users and select vehicle users who meet the requirements of sensing tasks.

[0117] The vehicle user (Smart Vehicular User, V) is a modern vehicle equipped with powerful computing, communication and storage functions, and has high mobility. Vehicle users need to ensure that their devices have sufficient performance to support various functions. They participate in sensing tasks to collect data and perform signature processing, and finally share data through edge nodes to obtain credit or rewards.

[0118] Specifically, the system is configured to disclose a parameter list of the system, specifically comprising: an input security parameter lambda, randomly selecting a large prime number q, determining a non-singular elliptic curve E: y 2 =x 3 +ax+b(mod q) (wherein, ), selecting a prime number n order cyclic group among all points (including the infinite point) of E and a generator selecting a secure hash function , a key derivation function KDF and a decompression function uncompress. The parameter list of the system is disclosed

[0119] User registration is configured for vehicle users to register their identities, and the sensing platform generates pseudonyms for them to protect their identity privacy while tracing the real identities of vehicles that violate the rules.

[0120] Task distribution is configured for the sensing platform to send sensing tasks to edge nodes, which then forward the tasks to adjacent vehicle user groups upon receiving the task instructions.

[0121] Task receiving is configured for vehicle users who voluntarily participate in the task to actively respond to the call of the edge node and upload their user data to the edge node.

[0122] Data transmission is configured for the edge node to further aggregate the digital signatures of users who complete the task, generate an aggregated signature, and interact with the sensing platform to negotiate and establish a session key.

[0123] Task auditing is configured for the sensing platform to verify the user signatures after receiving the result information and reward vehicle users who successfully submit valid data.

[0124] In the embodiment of the application, the SM2 digital signature-based key exchange protocol ensures the security of data transmission, including the correctness of signatures and session keys.

[0125] Application Example One: Data Transmission of the Vehicle-mounted Crowd Sensing System

[0126] By applying the data transmission method of the vehicle-mounted crowd sensing system of the application to the intelligent vehicle-mounted system, a more secure and reliable data transmission platform is provided for the vehicle-mounted crowd sensing system, attracting more vehicle users to participate, thereby providing more abundant and high-quality data services for data demanders and creating business value. The application can realize the following functions:

[0127] By applying the data transmission method of the vehicle-mounted crowd wisdom perception system of the present application to the intelligent vehicle-mounted system, the following functions can be realized:

[0128] Enhancing data security: By using SM2 digital signature technology, the security and integrity of data transmitted by vehicle users in the network are ensured, preventing illegal tampering or theft of data.

[0129] Protecting user privacy: By generating pseudonyms for vehicle users, their real identities are protected from unauthorized third parties, while their real identities can be traced back when necessary to deal with violations.

[0130] Improving user engagement: Through reward mechanisms and credit management, vehicle users are encouraged to actively participate in data collection and sharing, improving the quality and quantity of data.

[0131] Optimizing task allocation: The perception platform can effectively allocate data collection tasks to edge nodes, which in turn allocate tasks to nearby vehicle users, improving the efficiency and accuracy of task allocation.

[0132] Improving data processing capacity: Edge nodes preprocess and aggregate vehicle user data, reducing the computational burden of the perception platform and improving the overall data processing capacity of the system.

[0133] Ensuring data transmission efficiency: Through secure session key negotiation between edge nodes and the perception platform, data can be quickly and securely transmitted to the perception platform.

[0134] Enhancing system scalability: The system design allows for flexible handling of tasks of different scales, adapting to changing data demands and vehicle user numbers.

[0135] Promoting data service innovation: Providing richer and higher quality data services creates new business opportunities for data demanders and promotes the development of intelligent transportation systems and smart cities.

[0136] Implementing intelligent supervision: Through credit management and violation tracing mechanisms for vehicle users, intelligent supervision of vehicle behavior is realized, improving road safety.

[0137] Supporting decision-making: Providing real-time and accurate data support for urban traffic management, environmental monitoring, emergency response and other fields helps decision-makers make more scientific and reasonable decisions.

[0138] Promoting vehicle intelligence: Through the application of the vehicle-mounted crowd wisdom perception system, the development of vehicle intelligence is promoted, improving the autonomy and interoperability of vehicles.

[0139] Enhance user experience: by providing a safe and reliable data transmission platform, enhance the use experience of vehicle users, improve the satisfaction and loyalty of users to the intelligent vehicle system.

[0140] Through the display of the application embodiment, it can be seen that the application not only improves the performance of the vehicle-mounted crowd wisdom perception system, but also provides strong technical support for the construction of intelligent transportation and smart city, and creates significant economic and social value for participating vehicle users and data demanders.

[0141] The application embodiment has achieved some positive effects in the research and development or use process, and indeed has great advantages compared with the prior art, which will be described below in combination with the data and graphs of the test process.

[0142] In order to evaluate the prediction performance of the application comprehensively and in-depth, the calculation cost of the application is compared with the calculation cost of the methods of Tsai et al. [1] , Wu et al. [2] , Wang Fei et al. [3] , H. Lian et al. [4] and Wang Xiaohu et al. [5] Method. In order to clearly describe the theoretical analysis results, the article uses T em to represent the multiplication operation on the elliptic curve, T m to represent the multiplication operation on GF , T H to represent the secure hash operation, T E to represent the modular exponentiation operation and T P to represent the bilinear pair operation.

[0143] In the application, the required operations are T em +3T m +T H , and the total calculation cost is 2.378ms; in the method of Tsai et al. A leakage-resilient ID-based authenticated key exchange protocol with a revocation mechanism, the required operations are 15T P +33T E, the total calculation overhead is 214.215ms; in the method proposed by Wu et al. in An identity-based authenticated key exchange protocol resilient to continuous key leakage, the required operations are 4+27, and the total calculation overhead is 127.194ms; in the method proposed by Wang Fei et al. in Perfectly Forward-Secure Identity-Based Authenticated Key Agreement Scheme, the required operations are T P +8T E , the total calculation overhead is 36.611; in the method proposed by H. Lian et al. in Efficient and Strong Symmetric Password Authenticated Key Exchange With Identity Privacy for IoT, the required operations are 6T E +14T H , the total calculation overhead is 23.132ms; in the method proposed by Wang Xiaohu et al. in Identity-Based Authenticated Key Exchange Protocol Based on SM2, the required operations are 1T E +15T em , the total calculation overhead is 37.24ms. The comparison of calculation overheads of various methods is shown in Table 1. Figure 3 is the comparison of running time of the existing method and the method in the present application. The experimental analysis result shows that the time overhead of the present application is the smallest, and thus the present application is more suitable for the Internet of Vehicles and other resource-limited network environments.

[0144] Table 1 Performance comparison

[0145]

[0146]

[0147] The following are two specific embodiments of the vehicle-mounted crowd-sensing data encryption transmission method in the present application:

[0148] Embodiment 1: Traffic congestion information collection and transmission

[0149] 1. Task allocation and vehicle user selection: During the traffic peak period, the sensing platform SP receives the demand of the traffic management department, i.e. collecting the traffic congestion situation in a specific area. The SP distributes the collection task to the vehicle users located in the target area through the edge node EN. The EN selects the vehicle users V with high credit degree and in compliance with the conditions according to the credit degree prediction and the location of the vehicle users.

[0150] 2. Data collection and encrypted transmission: Selected vehicle users collect data such as speed, density, and traffic volume of road segments using on-board sensors during driving. After collecting data, each vehicle user generates a session key using a SM2-based digital signature key exchange protocol, signs and encrypts the data to ensure data integrity and confidentiality. The encrypted data is transmitted to the SP via EN.

[0151] 3. Data verification and rewards: After receiving the encrypted traffic data, the SP decrypts and verifies the data using the session key to ensure its authenticity and reliability. For vehicle users who complete the task, the SP gives corresponding credit and rewards, and records the pseudo-identity. If there is abnormal data, the SP can trace the real identity of the user through the pseudo-identity mechanism for tracing.

[0152] Example 2: Road safety condition monitoring

[0153] 1. Task allocation and pseudo-identity generation: To monitor the safety condition of a specific road, the SP receives a request from the traffic security department. The SP generates a collection task according to the demand and generates a pseudo-identity for each vehicle user before publishing the task to ensure anonymity. After receiving the task, EN allocates the collection task to eligible vehicle users V who pass the credit screening.

[0154] 2. Data collection and processing: Vehicle users collect real-time information of the road through on-board sensors, including road conditions, obstacles, traffic sign clarity, etc. During data collection and processing, vehicle users use SM2 digital signature encryption protocol to generate signatures and session keys to encrypt the collected data, ensuring data security and integrity.

[0155] 3. Data transmission and identity tracking: Encrypted data is uploaded to the SP through EN, and the SP evaluates the road safety condition after decrypting and verifying the data and provides rewards and credit score improvement to vehicle users who complete the task. If security risks are found in the data, the SP can trace the real identity of the corresponding vehicle user through the pseudo-identity mechanism to further obtain detailed information of the road segment or confirm the authenticity of the data source.

[0156] The vehicle-mounted group intelligent perception data encryption transmission method proposed in the present application solves the problems of data security, privacy protection, and data traceability in the process of vehicle-mounted perception data transmission in the prior art, and achieves significant technical progress. In traditional vehicle-mounted group intelligent perception data transmission systems, there are problems such as weak data security, difficulty in protecting user privacy, and low data transmission efficiency, which lead to risks such as user data leakage and data untraceability. The present application innovatively designs data encryption transmission and privacy protection, adopts a SM2-based digital signature key exchange protocol and a pseudo-identity generation mechanism, effectively solving the above problems.

[0157] Firstly, in terms of data security, the application adopts a key exchange protocol based on SM2 digital signature to ensure the encryption and integrity of data during transmission. Through the encryption signature mechanism of the session key, not only the security and correctness of data transmission can be ensured, but also the reliability of transmission can be guaranteed. This scheme significantly reduces the risk of data tampering or theft during transmission in the vehicle network, solves the problem of insufficient data transmission security in traditional vehicle group intelligence perception systems, and provides protection for efficient and reliable data transmission.

[0158] Secondly, in terms of user privacy protection, the application generates a pseudo-identity for each vehicle user on the perception platform to achieve anonymity, so that vehicle users do not need to expose their real identities when participating in perception tasks, avoiding the risk of privacy leakage. At the same time, the application allows the real identity of the user to be traced through the pseudo-identity in necessary cases, thus balancing privacy protection and traceability. Compared with the identity disclosure or complete anonymity mode in traditional methods, the pseudo-identity mechanism of the application improves user privacy protection while enhancing the flexibility and management ability of the system.

[0159] In terms of edge computing and task scheduling, the application uses edge nodes to intelligently distribute tasks, selects users that meet the requirements for perception tasks according to the credit of vehicle users, and improves the accuracy of tasks and the utilization rate of system resources. This design not only guarantees data quality, but also significantly improves data collection reliability, reduces system computing overhead and transmission load, and solves the problems of low task allocation efficiency and unstable data quality in traditional systems.

[0160] Finally, in terms of data transmission and incentive mechanism, the application establishes a data collection and reward mechanism, so that vehicle users can obtain credit points and rewards after completing perception tasks, encouraging users to actively participate in perception tasks. The perception platform rewards users who meet the requirements after verifying the authenticity and integrity of the data. This mechanism not only guarantees data quality, but also improves user participation and data collection coverage, providing impetus for the sustainable development of group intelligence perception data systems.

[0161] Overall, the application has made significant progress in data security, privacy protection, task scheduling and incentive mechanism, successfully improving the security, applicability and stability of the vehicle group intelligence perception system in a heterogeneous network environment, providing innovative technical support for the development of intelligent transportation systems and the Internet of Vehicles industry.

[0162] The above is only a specific implementation of the application, but the protection scope of the application is not limited to this. Any modification, equivalent replacement and improvement made by any person skilled in the art within the technical scope disclosed by the application, as long as it is within the spirit and principles of the application, should be covered within the protection scope of the application.

Claims

1. A method for encrypting and transmitting vehicle-mounted crowd intelligence perception data, characterized in that: The method comprises the following steps: Step 1: System settings, which is used to disclose the system parameter list; Step 2: User registration, which is used for the in-vehicle user to register their identity. The sensing platform generates a pseudonym for them to protect the user's identity privacy and to trace the true identity of the illegal vehicle. Step 3: Task distribution, where the sensing platform sends sensing tasks to the edge nodes. Once the task instructions are received, the edge nodes forward them to the neighboring vehicle user groups. Step 4: Task reception. Vehicle users interested in participating in the task will actively respond to the edge node's call and upload their user data to the edge node. These users then initiate crowdsensing activities and transmit the collected data results back to the edge node. Step 5: Data transmission. The edge node further aggregates the digital signatures of users who have completed the task, generates an aggregate signature, and securely interacts with the perception platform to negotiate and establish a session key. Using this session key, the edge node encrypts the perception data and securely transmits it to the perception platform. Step six, task review, is used to verify the user's signature after the perception platform task receives the result information, and give corresponding rewards to vehicle users who successfully submit valid data.

2. The method for encrypting and transmitting vehicle-mounted crowd intelligence perception data according to claim 1, wherein: The step one comprises: Input security parameter λ, randomly select a large prime number q, and determine the non-singular elliptic curve E:y 2 =x 3 +ax+b(mod q), where Select a prime n-order cyclic group from all points in E and generators All points include the point at infinity; select a secure hash function Key derivation function KDF and decompression function uncompress; public system parameter list 3. The method for encrypting and transmitting vehicle-mounted crowd intelligence perception data according to claim 2, wherein: The second step includes: During the user registration phase, the specific process between user V and the perception platform SP is as follows: The user first randomly selects Then calculate P = d·G; let the user's private key be sk v =d, user public key is pk v =P; use the public key pk of the perception platform SP Encrypt the information M, H(M), and pk submitted during registration v and random number a, and send the encrypted result to SP, where M=(ID v ||m * ), ID v is the real identity of the user, m * For the user's personal information data, H(M) ensures the integrity of the user's registration information; SP→V:E a [ID' v ], After receiving the encrypted information, SP uses its private key sk SP Decrypt and get M, H(M), pk v and random number a, verify the integrity of M through H(M); after verification, SP generates a pseudonymous ID for the user v ', calculate ID v '=H(ID v ), and encrypt it with a and send it to the user. After receiving it, the user decrypts it with a to get the ID v ', and subsequently use the pseudonym for communication; when tracing the user of the responsibility issue, the SP presents or exposes the user's real identity.

4. The method for encrypting and transmitting vehicle-mounted crowd intelligence perception data according to claim 3, wherein: The step three includes: Task distribution is a key step in the interaction between the perception platform SP, edge nodes EN, and users V. The specific process is as follows: σ Task =Sign(sk SP ,Task), SP first creates task information Task = (work||re||cr th ||T1), where work is the task details, re is the reward for completing the task, and cr th is the credit threshold required by the task for the user's credit, T1 is the current timestamp; then, SP calculates H(Task) and uses its private key sk SP Sign the task and generate σ Task ;Verify(pk SP ,σ Task ), SP distributes the task information with signature to nearby ENs; after receiving the information, EN calculates H(Task) to verify the integrity of the task information and uses pk SP Verify the signature to ensure the authenticity of the task information; if the verification is successful, EN will forward the task information to surrounding users; otherwise, EN will discard the task information to prevent the spread of errors.

5. The method for encrypting and transmitting vehicle-mounted crowd intelligence perception data according to claim 4, wherein: The fourth step includes: Task acceptance is the initial stage of user participation in the perception task. The specific process is as follows: According to the task requirements, it is assumed that a user willing to participate is the γth user, 1≤γ≤n, and the task information is verified as EN to ensure the integrity and authenticity of the task information; if the verification is passed, the user randomly selects a random number And calculate K γ =k γ G=(x γ ,y γ ), e γ =H(w γ ),r γ =(e γ +x γ )(mod n);w γ is the request information of the γth user participating in the perception task, that is, in is the pseudonym of the γth user, is the personal information data of the γth user, T2 is the current timestamp, and b is a random number; if r γ =0 or r γ +k γ =0, then reselect k γ Calculate again; otherwise calculate s γ =(1+d γ ) -1 (k γ -r γ d γ )(mod n); if s γ =0, then return to reselect the random number, otherwise generate the signature σ γ =(r γ ,s γ )=Sign(d γ ,w γ ); the user then uses pk EN Encryption γ and w γ , and send the encrypted result to EN; After receiving the encrypted information, EN uses its private key to decrypt it and obtains σ γ and w γ First, determine whether the time interval △T=T2-T1 is within the threshold range. If it exceeds the threshold, the data is discarded; if it is within the threshold, EN check Is it true? If not, the verification fails; otherwise, calculate e' γ =H(w' γ ) to test w' γ The integrity of γ ≠0, then further calculate t γ =(r γ '+s' γ )(mod n),K γ '=s' γ G+t γ P γ =(x' γ ,y' γ ), r γ '=(e' γ +x' γ )(mod n); Finally, test r γ '=r γ Is it established? If it is established, confirm the signature σ γ efficient.

6. The method for encrypting and transmitting vehicle-mounted crowd intelligence perception data according to claim 5, wherein: The step five includes: T E Stores EN's transaction perception data, T s The task reward re of SP is stored; EN uses the private key to calculate the random number k in the signature E =(s E (1+d E )+r E d E )(mod n), calculate x s =(r s -H(T s ))(mod n), transaction public key shared secrets Calculate and output the session key SK E =KDF(x ES ); SP uses the private key to calculate the random number k in the signature S =(s S (1+d S )+r S d S )(mod n), calculate x E =(r E -H(T E ))(modn), transaction public key Calculating the common secret Calculate and output the session key SK S =KDF(x ES ) EN sends the sensing task data, user pseudonym group and signature group collected from the user to SP through the session key.

7. The method for encrypting and transmitting vehicle-mounted crowd intelligence perception data according to claim 6, wherein: The step six comprises: SP uses the session key to decrypt and obtain the task result information. First, it determines whether the time interval △T=T2-T1 is within the threshold range. If it is not within the threshold range, the data is discarded. Otherwise, the data is If the information is verified to be true, batch verification of n user signatures (σ1,σ2,...,σ η ) validity; SP test Is it true? If not, the verification fails. Otherwise, for i=1,2,...,η, calculate in sequence: Test t γ ≠0 is true, if true then calculate e' γ =H(w γ ) to verify w γ The integrity of the calculation test Is it established? If so, sign is a valid signature; SP gives a certain amount of reward re to all users in the group and increases their credibility.

8. A vehicle-mounted crowd-sensing data encryption and transmission system according to the method of claim 1, characterized in that: include: System settings module: used to disclose the system parameter list; User registration module: used for in-vehicle user identity registration. The sensing platform generates a pseudonym for the user to protect the user's identity privacy and can also track the true identity of the illegal vehicle; Task distribution module: used by the perception platform to send perception tasks to edge nodes. Once the task instruction is received, the edge node will forward it to the neighboring vehicle user group. Task receiving module: Vehicle users interested in participating in the task will actively respond to the edge node's call and upload their user data to the edge node. These users then initiate crowdsensing activities and transmit the collected data results back to the edge node; Data transmission module: This module is used by edge nodes to further aggregate the digital signatures of users who have completed tasks, generate an aggregate signature, and securely interact with the perception platform to negotiate and establish a session key. Using this session key, the edge node encrypts the perception data and securely transmits it to the perception platform. The task review module is used to verify the user's signature after receiving the result information of the sensing platform task, and give corresponding rewards to the vehicle user who successfully submits valid data.

Images