Reliable road condition message transmission method based on vehicle-mounted fog calculation

Abstract

The invention discloses a reliable road condition message transmission method based on vehicle-mounted fog computing. The method comprises a registration stage, a mutual authentication and key negotiation stage, a road condition monitoring stage and a partial private key updating stage. According to the method, an authentication mechanism of the vehicle and the fog node for ensuring secure communication between the vehicle and the FN is provided without real-time participation of a trusted authority TA, the defects of long message transmission time and low message authentication and integrity in a traditional anonymous authentication protocol are overcome, the system efficiency is improved, and the communication overhead is reduced. A lightweight vehicle privacy security protection authentication scheme based on fog computing is provided to protect the vehicle privacy and minimize the vehicle storage burden. Under the condition that the real identity of the vehicle is not leaked, the identity information generation mode and the session key of the V2F are redesigned, the problems that identity authentication between the V2Fs is complex, the identity information storage amount is large, and communication messages are prone to attack are solved, the communication safety is improved, and the vehicle storage burden is relieved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a reliable road condition message transmission method based on vehicle-mounted fog computing. Background Art

[0002] The deep integration of connected vehicle (IoV) technology and network data security technologies is significantly driving the development of smart mobility and heralding a new paradigm for future social lifestyles. The continued evolution of IoV technology is deepening our understanding of intelligent, green, and safe driving. This technology enables real-time communication between vehicles, between vehicles and road infrastructure, and between vehicles and cloud data centers. The application of this real-time communication data enables more accurate traffic information, smarter navigation services, and promotes the practical application of autonomous driving technology, significantly improving driving safety and convenience. In particular, as electric vehicles and autonomous driving technologies continue to expand on a large scale, IoV technology will play a crucial role in the overall intelligent transportation system. In summary, the deep integration of IoV technology and network data security technologies will jointly build a safer and smarter future transportation ecosystem, profoundly transform our travel patterns, and significantly advance the development of intelligent transportation and smart cities.

[0003] 1. Research Status of Privacy Preservation Protocols in Vehicular Fog Computing

[0004] As the number of intelligent wireless sensor devices connected to the Internet of Vehicles (IoV) increases, the demand for real-time processing and secure storage of massive amounts of data is increasing. However, the centralized data processing architecture of in-vehicle cloud computing can hinder the timely processing of urgent and latency-sensitive messages due to long data transmission delays, potentially leading to severe traffic congestion or accidents. To address this, fog computing, with its advantages of low latency, decentralization, geographic distribution, location awareness, and real-time interaction, has been introduced into the IoV. In this type of fog computing-based IoV, fog nodes equipped with sufficient computing, storage, and communication resources are deployed at the road edge. This allows them to directly process data from vehicle terminals locally, reducing the burden on the cloud while improving data transmission efficiency and reducing service response latency. However, fog nodes deployed at the network edge are highly vulnerable to physical attacks, and the open communication environment also makes in-vehicle fog computing susceptible to various security and privacy attacks. Therefore, ensuring the security, privacy, and reliability of transmitted data has become a key concern in research on in-vehicle fog computing.

[0005] Researchers at home and abroad have conducted a series of studies on the security and privacy of data transmission in vehicular fog computing. Recently, Cui et al. used a fog computing framework that supports mobility, low latency, and location awareness, combined with online / offline encryption (OOE) technology, to propose an efficient and secure certificateless aggregate signcryption protocol to enhance the security of data transmission and improve the efficiency of message signing and authentication in road condition monitoring systems. In reality, fog servers and corresponding devices deployed at the edge of the network are extremely vulnerable to physical damage, and malicious attackers attempt to steal private vehicle information stored in fog devices. To address this issue, Xia et al. designed a conditional privacy-preserving authentication protocol based on multi-fog server collaboration in vehicular fog computing. This protocol achieves vehicle identity authentication and vehicle data privacy protection. Furthermore, even in the event of a small number of fog servers being damaged or the presence of malicious vehicles, the data and identity privacy of legitimate vehicles remain intact. However, their protocol uses time-consuming bilinear pairing operations and the Shamir secret sharing algorithm, which hinders efficient communication between vehicles and fog servers. To address certificate, pseudonym management, and key escrow issues and reduce system overhead, Yang et al. proposed a privacy-preserving aggregate authentication protocol for connected vehicle (IoV) security warning systems. This protocol allows vehicles to encrypt messages using symmetric keys distributed by their authorized agents (TAs), ensuring secure message transmission. Considering that fog nodes may receive numerous ciphertexts from surrounding vehicles in a short period of time, to reduce communication and storage overhead, fog nodes are allowed to aggregate the ciphertexts signed by surrounding vehicles into a single aggregate ciphertext using an anonymous, certificateless aggregate signcryption algorithm and transmit it to a cloud server. To address the computational inefficiency and single-point-of-failure issues of existing protocols, He et al. proposed a conditional privacy-preserving authentication protocol based on fog computing and multiple authorized agents (TAs). This protocol first performs three-way mutual authentication between the vehicle, fog node, and authorized agent (TA), and then enables the fog node to distribute a pseudonym set to the vehicle. The pseudonym and signature mechanisms ensure the security and privacy of vehicle communications, and the application of fog computing improves the protocol's computational and communication efficiency. However, mutual authentication between vehicles and fog nodes requires the TA's online, real-time participation, which increases the computational and communication overhead of the authentication process and makes it unsuitable for IoV scenarios with strict latency requirements. Zhang et al. designed an efficient, privacy-preserving vehicle network access control protocol based on Pederson commitments and Timed Efficient Stream Loss-Tolerant Authentication (TESLA), significantly improving the efficiency of authentication between vehicles and fog nodes. In their protocol, fog nodes are required to store the access credentials of all vehicles in order to achieve mutual authentication between vehicles and fog nodes without the involvement of the vehicle owner.In reality, not all vehicles pass through a fog node. Requiring fog nodes to store the access credentials of all vehicles in advance would significantly increase the storage burden on the fog nodes. However, the lightweight privacy-preserving V2I authentication protocol designed by Lv et al. can significantly reduce the storage burden on the infrastructure. The vehicle first uses the Dijkstra algorithm to predict its path on the actual map. It then uses Moore's curve technology to convert all roadside units that appear on the path into vectors. This vector is encrypted using the BGN homomorphic mechanism and sent to the Certificate Authority (CA). Ultimately, the CA sends the access credentials for all roadside units on the vehicle's path to the vehicle without knowing the vehicle's specific travel path. Although Lv et al. claim that V2I authentication will not reveal the vehicle's specific travel path to the CA, the CA, which knows the true identity of all vehicles, can still obtain the vehicle's travel path through the vehicle's pseudonym.

[0006] In summary, most existing protocols based on mutual authentication between vehicles and fog nodes still require real-time online participation by the vehicles. Furthermore, some protocols that protect vehicle identity privacy through pseudonyms ignore the burden of storing pseudonyms. Furthermore, many protocols fail to consider the reliability of vehicle-provided data. Therefore, if some vehicles send misleading messages due to observation errors, the system will be unable to identify the most reliable messages.

[0007] 2. Research status of data transmission protocols for the Internet of Vehicles

[0008] The open nature of the Internet of Vehicles (IoV) network makes it highly vulnerable to various security attacks. Therefore, to ensure the security of data transmission within the IoV, authentication, confidentiality, and non-repudiation are often used to measure the effectiveness of a data transmission protocol. Confidentiality is primarily achieved through cryptographic algorithms (public key infrastructure and symmetric cryptographic algorithms), while authentication and non-repudiation are primarily verified through digital signatures. In recent years, many researchers have conducted research on IoV-specific data transmission protocols. To ensure that messages from trusted institutions within the IoV system are efficiently and securely transmitted to vehicle terminals, Vijayakumar et al. designed a secure data transmission protocol for the IoV based on mutual authentication and dual key management. In this protocol, mutual authentication ensures that only authorized vehicles can participate in IoV communications. Furthermore, the trusted institution (TA) within the system generates different group keys for two different user groups: one group key is used to transmit information from the TA to the primary user group, and the other group key is used by the primary user group to transmit messages to the secondary user group. The efficient update of these two group keys is achieved using the Chinese Remainder Theorem. In order to save system communication and computing costs while ensuring the security of data transmission, Joshi et al. proposed a secure and efficient data transmission protocol for the Internet of Vehicles, which mainly uses the RSA encryption algorithm to achieve secure data transmission between different communication nodes. Introducing cloud computing into the Internet of Vehicles can further expand the computing and storage capabilities of the Internet of Vehicles, improve communication efficiency and system security. However, the car-cloud system still inherits the characteristics of the traditional Internet of Vehicles, such as openness and high-speed mobility of vehicles. Therefore, in order to ensure the security of data transmission in the car-cloud, Zhang et al. [9] designed a data publishing protocol with security and privacy through technologies such as the Asymmetric Group Key Agreement Protocol (AGKAP) and the Location-based Encryption (LBE) algorithm. The protocol allows vehicles to encrypt data with a group key and publish it anonymously to the car-cloud system. The identity of the vehicle and the data sent cannot be obtained by entities on the public channel, which can protect user privacy and ensure data confidentiality. Their protocol requires all members in the group to execute the protocol honestly, because once one person compromises and leaks the group key, it will pose a serious threat to the communication security of the entire group. Subsequently, Amin et al. designed a public session key agreement protocol for V2V secure communication based on elliptic curve cryptography and hash functions. Vehicles can use the negotiated session key to encrypt and transmit data. This protocol ensures the confidentiality and security of data transmitted on public channels.To address the redundancy issue that arises when a trusted authority within a connected vehicle system sends ciphertext of the same message to multiple vehicles, Zhong et al. designed a secure data sharing protocol for vehicle-to-everything (V2I) communication based on identity-based broadcast encryption (IBBE). This protocol allows a trusted authority to generate fixed-length ciphertext for a group of vehicles with a single encryption, ensuring both data security and data sharing efficiency. Similarly, to improve data encryption efficiency and address the single point of failure inherent in a single trusted authority model, Wei et al. integrated Cuckoo filters into a multi-trusted authority model and then designed a lightweight, privacy-preserving authenticated key agreement protocol based on symmetric cryptography. Vehicles can then encrypt transmitted data using the negotiated session key. Therefore, attackers without the session key cannot obtain the corresponding plaintext message, ensuring secure data transmission.

[0009] While these protocols, based on technologies like public key infrastructure and symmetric cryptographic algorithms, ensure confidentiality in IoV data transmission, several challenges remain. For one thing, data is visible only to specific users (the vehicle or cloud service provider), preventing true data sharing. Furthermore, cloud service providers are often not fully trustworthy, and storing all user data on them could pose a threat to user privacy. Therefore, ensuring data security while enabling data manipulation and application remains a pressing challenge in IoV data transmission.

[0010] 3. Communication protocol for setting system thresholds

[0011] Many protocols fail to consider the reliability of vehicle messages. This means they are unable to identify and process the most reliable vehicle messages when some vehicles send misleading messages due to observation errors. Inspired by this, Wang et al. and Chen et al. proposed the concept of thresholds and designed threshold-based anonymous verification protocols. Their protocols require the receiver to only process and receive the same message reported by more than w different vehicles, effectively addressing the reliability issue in road condition message transmission.

[0012] However, although they introduced the concept of threshold, they did not provide a method to determine the specific threshold w. Summary of the Invention

[0013] The technical problem to be solved by the present invention is that the existing authentication mechanism has problems such as overly strong security assumptions, heavy storage burden, high latency and centralized management, and efficiency and security cannot be well guaranteed. In order to solve the above problems, a reliable road condition message transmission method based on vehicle-mounted fog computing is provided.

[0014] The object of the present invention is achieved in the following manner:

[0015] A reliable road condition message transmission method based on vehicle-mounted fog computing includes a registration phase, a mutual authentication and key negotiation phase, a road condition monitoring phase, and a partial private key update phase.

[0016] The registration phase refers to the registration of both vehicles and fog nodes (FNs) before they enter the road condition communication system. For vehicles, the TA generates a partial private key during the vehicle registration phase, and the vehicle can generate a full private key based on the partial private key. Unlike vehicles, a long-term public-private key pair is generated for fog nodes (FNs) during this phase. The registration phase is conducted over a secure communication channel. The registration of vehicles and fog nodes (FNs) is as follows:

[0017] Vehicle Registration:

[0018] Step 11: Get a real identity RID i Vehicle v i , choose a random number And calculate the vi part of the public key A i =a i p, where q and p are the order and generator of the additive cyclic group G based on the elliptic curve; finally, the vehicle sends <RIDi,A i > to him;

[0019] Step 12: When TA receives vehicle v i When registering a request, a random number is selected And calculate the vi part of the public key B i =b i Partial private key information of p and vi Where h0 is the hash function SHA-2; then, according to the system master private key s, TA is the vehicle v i Generate a partial private key c i , c i =b i +s.h1(A i ll B i )mod q, h1 is the hash function SHA-2; Finally, TA sends <c i , C i >Will be preloaded into the vehicle's tamper-proof device and stored in a local database <RID i , c i >

[0020] Step 13: Vehicle v i Generate its complete private key (a i , c i ) and the corresponding public key (Ai , B i );

[0021] Fog node FN registration:

[0022] Step 21: j-th fog node FN j , submit your own identity IDR to TA to request registration;

[0023] Step 22: Receive FN j The TA who registered the request selects a random number As FN j The private key is calculated; the corresponding public key D j =d j p and sk j =d j +s·h2(ID fj ||D j )mod q, h2 is the hash function SHA-2; then, TA converts the long-term public and private key pair <D j ,sk j >Send to FN j ;

[0024] Step 23: FN j Secret Storage <D j ,sk j >.

[0025] In the mutual authentication and key negotiation phase, the vehicle and the fog node FN need to enter the mutual authentication and key negotiation phase; when the vehicle v i Enter FN j When the communication range is i and FN j Quickly complete mutual authentication and negotiate a session key for subsequent secure communication; i and FN j The detailed steps of mutual authentication and key agreement are shown below:

[0026] Single authentication:

[0027] Step 31: When the vehicle v i Enter FN j When the communication range is within, randomly select an integer And calculate X i =x i p; then v i Generate pseudonymous PID i ,Right now Where T i Indicates the current timestamp, p pub is the system public key, h3 is the hash function SHA-2; then vi Generate signature σ i1 , where σ i1 =c i +a i +x i a i mod q and a i =h2((PID i ||B i ||X i ||T i ); Finally, v i Will <PID i , X i , A i , B i ,σ i1 , T i >Send to FN j ;

[0028] Step 32: After receiving v i Messages sent <PID i , X i , A i , B i ,σ i1 , T i >Followed by FN j First check the timestamp T i The timeliness of v i Time and FN of sending the message j The time of receiving the message is T i and T F , and use ΔT to represent the effective interval time of sending and receiving messages set by the system; if T F -T i ≥ΔT,FN j Discard the message; otherwise check the signature σ i1 The validity of , verify equation (1-1):

[0029] σ i1 p=B i +p pub h1(A i ||B i )+A i +x i a i (1-1)

[0030] Among them: a i =h2(PID i ||B i ||X i ||T i );

[0031] The correctness of equation (1-1) is proved below:

[0032]

[0033] If equation (1-1) holds true, FN j Pick a random number And calculate Y j =y j p; then, FN j Generate and v i The session key sk ji , that is, sk ji =h3(y j X i ||PID i ||ID fj ); Finally, FN j Calculating β j1 =h2(sk ji ||Y j ||T j ), generate signature σ j1 =sk j +β j y j mod q, and send <ID fj , Y j , D j ,σ j1 , T j > give v i , where T j is the current timestamp;

[0034] Step 33: v i Receive FN j Messages sent <ID fj , Y j , D j ,σ j1 , T j >After, v i First check the timestamp T j The validity of j Invalid, v i The message will be discarded; otherwise v i Calculate the session key sk ij =h3(x i y j ||PID i ||ID fj ); Then, calculate β j1 =h2(sk ij ||Y j ||T j) to check whether equation (1-3) is true; if not, reject the message; otherwise v i Receive the message;

[0035] δ j1 p=D j +p pub h2(ID fj ||Dj)+β j1 y j (1-3) The detailed proof process of the correctness of equation (5-3) is given below:

[0036]

[0037] So far, v i With FN j Successfully verified the legitimacy of each other's identities and negotiated a session key sk = sk ij =sk ji The session key sk negotiated in this process can be used to ensure v i and FN j Safety communication during the road condition monitoring phase;

[0038] Batch certification:

[0039] The fog node FN receives the pseudonyms {PID1, PID2, ..., PID k When a message is signed by k vehicles, batch authentication technology is used to aggregate k signatures into one signature. Subsequently, FN authenticates the single signature. Compared with authenticating k signatures separately, batch authentication technology is used to authenticate the signatures of k vehicles at one time, which greatly improves the authentication efficiency of FN. The details of FN batch authentication of k vehicle signatures are described as follows:

[0040] Step 41: Fog node FN checks timestamp T i The validity of , where i∈(1, k); if T i If the valid time interval is exceeded, FN refuses to accept all k signatures; otherwise, FN continues to perform the following steps;

[0041] Step 42: FN selects a vector u={u1,…,u i ,…,u k},u i Is FN from the interval [1, 2 t ] is randomly selected and t is a small integer; the computational cost of this process is negligible; then, FN checks whether equation (1-5) holds;

[0042]

[0043] If equation (1-5) does not hold, it means that there are one or more invalid signatures among the signatures of these k vehicles. FN can use binary search technology to find all invalid signatures; otherwise, FN receives the signatures of these k vehicles and continues to execute the following steps;

[0044] The correctness proof of equation (1-5) is shown below:

[0045]

[0046] The road condition monitoring stage specifically includes:

[0047] Step 51: When the vehicle v i Located at fog node FN j When reporting traffic information within the communication range, first calculate the traffic information M i＝ h(m i ll t i ), h is the hash function, m i Indicates traffic information and t i Represents the current timestamp; to ensure m i Confidentiality of data uploaded in public channels,Vehicle V i Encrypt m with the session key SK i Then use ES encryption operation to obtain C i =ES sk (m i llt i ll M i ); Finally, v i Send C i To FN j ;

[0048] Step 52: Receive V i The C sent i After that, fog node FN j Execute the symmetric decryption algorithm to obtain C i The corresponding plain text traffic information (m i ll t i ll M i )=DS sk (C i ); Then, the fog node FN j Check timestamp t i Is it within the valid time interval, and through the equation h(m i ll t i )=M i Verify the integrity of the message;

[0049] Step 53: FN j Only when at least w different vehicles report message mi , the traffic condition message will be received and processed, where w is the threshold determined by the system; otherwise, FN j Refuse to receive m i .

[0050] The partial private key update phase: When the vehicle V i In the fog node FN j When requesting to update some private keys within the communication range, TA will j Update V with the help of i The specific steps are as follows:

[0051] Step 61: Vehicle v i Select random number As v i Part of the private key and calculate v i New partial public key Then, V i Get a new pseudonym Encrypted with session key sk and send Give fog node FN j ;

[0052] Step 62: FN j Decrypt with session key sk Get the corresponding plaintext and deliver Give TA to a trusted institution;

[0053] Step 63: After receiving FN j After sending the message, TA first selects a random number And calculate v i New partial public key and public key related information Then, TA is v i Calculate a new partial private key; by calculating the partial private key modq, and finally TA uses h0(RID i )encryption Parameters and After obtaining and send To FN j ;

[0054] Step 64: FN j Send further Give vehicle v i , so far, v i Get the updated partial private key Then, get the new complete private key and the corresponding public key

[0055] The present invention proposes a vehicle-to-fog node (V2F) authentication mechanism for secure communication between vehicles and fog nodes (FNs) without requiring real-time participation from a trusted authority (TA). This mechanism addresses the drawbacks of traditional anonymous authentication protocols, such as long message transmission times and weak message authentication and integrity, improving system efficiency and reducing communication overhead. A lightweight vehicle privacy and security authentication scheme based on fog computing is proposed to protect vehicle privacy and minimize vehicle storage burdens. The V2F identity information generation method and session keys are redesigned without revealing the vehicle's true identity, addressing complex V2F identity authentication, large identity information storage requirements, and vulnerability to attack in communication messages. This improves communication security and reduces vehicle storage burdens. Using real datasets, a threshold-based anonymous verification communication protocol is designed to set the system threshold. In the event that some vehicles send misleading messages, the system can respond and handle the number of vehicles reporting the same message differently, addressing the issue of false road condition reports caused by factors such as vehicle observation angle, distance, and time, and improving the reliability of road condition reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the method of the present invention.

[0057] Figure 2 It is the communication model of the present invention.

[0058] Figure 3 This is the P / R curve of Guangzhou, Shanghai and Shenzhen

[0059] Figure 4 This is the P / R curve of Guangzhou, Shanghai and Shenzhen under different w.

[0060] Figure 5 It is the technical roadmap of the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same technical meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0063] like Figure 1As shown, the present invention provides a reliable road condition message transmission method based on vehicle-mounted fog computing (hereinafter referred to as VFAS), which includes a registration phase, a mutual authentication and key negotiation phase, a road condition monitoring phase and a partial private key update phase.

[0064] To implement a V2F authentication protocol that does not require the real-time participation of the TA, and to change the situation where both the FN and the vehicle's identity information are stored in the TA, we mainly conduct research from the following two aspects:

[0065] First, both vehicles and fog nodes (FNs) must register with the TA before entering the traffic communication system. Finally, they must pass verification before they can report traffic information as legitimate users. For vehicles, the TA generates a partial private key during the vehicle registration phase, which the vehicle can then use to generate a full private key. Unlike vehicles, a long-term public-private key pair is generated for fog nodes (FNs) during this phase.

[0066] Secondly, to ensure the confidentiality of road information during the interaction between the vehicle and FN, the two communicating parties need to enter the mutual authentication and key negotiation phase. i Enter FN j When the communication range is i and FN j Quickly complete mutual authentication and negotiate the session key for subsequent secure communication. Due to the high-speed mobility of the vehicle, ensure v i and FN j The efficiency of inter-authentication is crucial.

[0067] The registration phase is carried out on a secure communication channel. The registration of vehicles and fog nodes FN is as follows:

[0068] Vehicle Registration:

[0069] Step 11: Get a real identity RID i Vehicle v i , choose a random number And calculate v i Partial public key A i =a i p, q, p are the order and generator of the additive cyclic group G based on the elliptic curve; finally, the vehicle sends<RIDi,Ai> Give it to him / her;

[0070] Step 12: When TA receives vehicle v i When registering a request, a random number is selected And calculate v i Partial public key B i =b i p and v i Partial private key information h0 is the hash function SHA-2; then, TA is the vehicle v i Generate a partial private key c i , c i =b i +s.h1(A i ll B i )mod q, h1 is the hash function SHA-2; Finally, TA sends <c i , C i >Will be preloaded into the vehicle's tamper-proof device and stored in a local database <RID i , c i >

[0071] Step 13: Vehicle v i Generate its complete private key (a i , c i ) and the corresponding public key (A i , B i ); the registration service can be executed according to the predetermined procedures, such as the annual vehicle inspection public key (A i , B i ); Registration services can be performed according to predetermined procedures, such as annual vehicle inspections.

[0072] Fog node FN registration:

[0073] Step 21: j-th fog node FN j , and change your ID R Submit to TA to request registration;

[0074] Step 22: Receive FN j The TA who registered the request selects a random number As FN j The private key is calculated; the corresponding public key D j =d j p and sk j =d j +s·h2(ID fj ||D j )mod q, h2 is the hash function SHA-2; then, TA converts the long-term public and private key pair <D j ,sk j >Send to FN j ;

[0075] Step 23: FN j Secret Storage <D j ,sk j >.

[0076] Furthermore, in the mutual authentication and key negotiation phase, the vehicle and the fog node FN need to enter the mutual authentication and key negotiation phase; when the vehicle v i Enter FN j When the communication range is i and FN j Quickly complete mutual authentication and negotiate a session key for subsequent secure communication; i and FN j The detailed steps of mutual authentication and key agreement are shown below:

[0077] Single authentication:

[0078] Step 31: When the vehicle v i Enter FN j When the communication range is within, randomly select an integer And calculate X i =x i p; then v i Generate pseudonymous PID i ,Right now p pub is the system public key, h3 is the hash function SHA-2, where T i Indicates the current timestamp; it is worth noting that in this protocol, v i Generate pseudonymous pID i The operation can be performed offline. Then v i Generate signature σ i1 , where σ i1 =c i +a i +x i a i mod q and a i =h2(PID i ||B i ||X i ||T i ); Finally, v i Will <PID i , X i , A i , B i ,σ i1 , T i >Send to FN j ;

[0079] Step 32: After receiving v i Messages sent <PID i , X i , A i , B i ,σ i1 , T i >Followed by FNj First check the timestamp T i The timeliness of v i Time and FN of sending the message j The time of receiving the message is T i and T F , and use ΔT to represent the effective interval time of sending and receiving messages set by the system; if T F -T i ≥ΔT,FN j Discard the message; otherwise check the signature σ i1 The validity of , verify equation (1-1):

[0080] σ i1 p=B i +p pub h1(A i ||B i )+A i +x i a i (1-1)

[0081] Among them: a i =h2(PID i ||B i ||X i ||T i );

[0082] The correctness of equation (1-1) is proved below:

[0083]

[0084] If equation (1-1) holds true, FN j Pick a random number And calculate Y j =y j p; then, FN j Generate and v i The session key sk ji , that is, sk ji =h3(y j X i ||PID i ||ID fj ); Finally, FN j Calculating β j1 =h2(sk ji ||Y j ||T j ), generate signature σ j1 =sk j +β j y j mod q, and send <IDfj , Y j , D j ,σ j1 , T j > give v i , where T j is the current timestamp;

[0085] Step 33: v i Receive FN j Messages sent <ID fj , Y j , D j ,σ j1 , T j >After, v i First check the timestamp T j The validity of j Invalid, v i The message will be discarded; otherwise v i Calculate the session key sk ij =h3(x i y j ||PID i ||ID fj ). Then, calculate β j1 =h2(sk ij ||Y j ||T j ) to check whether equation (1-3) is true; if not, reject the message; otherwise v i Receive the message;

[0086] σ j1 p=D j +p pub h2(ID fj ||D j )+β j1 y j (1-3)

[0087] The following is a detailed proof of the correctness of equation (5-3):

[0088]

[0089] So far, v i With FN j The legitimacy of each other's identities is successfully verified, and a session key sk= is negotiated.

[0090] sk ij =sk ji The session key sk negotiated in this process can be used to ensure v i and FN j In road condition monitoring

[0091] secure communications during the phase;

[0092] Batch certification:

[0093] The fog node FN receives the pseudonyms {PID1, PID2, ..., PID k}, batch authentication technology is used to aggregate k signatures into one signature; then, FN authenticates the single signature; compared with authenticating k signatures separately, batch authentication technology is used to authenticate the signatures of k vehicles at one time, which greatly improves the authentication efficiency of FN; details of FN batch authentication of k vehicle signatures

[0094] The description is as follows:

[0095] Step 4 1: Fog node FN checks timestamp T i The validity of , where i∈(1, k); if T i If the valid time interval is exceeded, FN refuses to accept all k signatures; otherwise, FN continues to perform the following steps;

[0096] Step 42: FN selects a vector u={u1,…,u i ,…,u k},u i Is FN from the interval [1, 2 t ] is randomly selected and t is a small integer. The computational cost of this process is negligible; then, FN checks whether equation (1-5) holds;

[0097]

[0098] If equation (1-5) does not hold, it means that there are one or more invalid signatures among the signatures of these k vehicles. FN can use binary search technology to find all invalid signatures; otherwise, FN receives the signatures of these k vehicles and continues to execute the following steps;

[0099] The correctness proof of equation (1-5) is shown below:

[0100]

[0101] like Figure 2 To ensure V2F security authentication for secure communication between vehicles and FNs, a communication model for a reliable road condition message transmission protocol in vehicular fog computing is designed. As shown in the figure below, the application layer includes the trusted authority (TA) and the application center, the fog nodes form the platform layer, and the vehicle network layer primarily consists of vehicles. Through the collaboration of these three layers, secure transmission and application of traffic data is achieved.

[0102] To implement a lightweight vehicle privacy protection scheme based on fog computing, we conducted research from the following aspects. First, monitoring road conditions. In this phase, we use hash functions and the session key negotiated in the previous phase to ensure the confidentiality and reliability of vehicle road condition information. In other words, vehicle vi, which holds the session key SK, reports current road condition information (such as road collapse, traffic congestion, snow on the road, etc.) to the fog node FN, which holds the same session key. j In order to avoid the occurrence of false road condition reports due to factors such as the vehicle's observation angle, distance, and time, FN j Only process and receive the same message m sent by more than a threshold w different vehicles i .

[0103] The road condition monitoring stage specifically includes:

[0104] Step 51: When the vehicle v i Located at fog node FN j When reporting traffic information within the communication range, first calculate the traffic information M i＝ h(m i ll t i ), h is the hash function, m i Indicates traffic information and t i Represents the current timestamp; to ensure m i Confidentiality of data uploaded in public channels,Vehicle V i Encrypt m with the session key SK i Then use ES encryption operation to obtain C i =ES sk (m i llt i ll M i ); Finally, v i Send C i To FN j ;

[0105] Step 52: Receive v i The C sent i After that, fog node FN j Execute the symmetric decryption algorithm to obtain C i The corresponding plain text traffic information (m i ll t i ll M i )=DS sk (C i ); Then, the fog node FN j Check timestamp t i Is it within the valid time interval, and through the equation h(m i ll t i )=Mi Verify the integrity of the message;

[0106] Step 53: FN j Only when at least w different vehicles report message m i , the traffic condition message will be received and processed, where w is the threshold determined by the system; otherwise, FN j Refuse to receive m i .

[0107] Secondly, partial private key update. The partial private key of the vehicle is an important parameter for maintaining vehicle security communication. In order to further strengthen the security and privacy protection of the vehicle, the present invention provides a partial private key update operation for the vehicle. Due to the limited communication range of the vehicle, when the vehicle is close to the TA, it can communicate directly with it. Therefore, this stage only considers the scenario where the vehicle needs to communicate with the TA through FN. When the vehicle v i In the fog node FN j When requesting to update some private keys within the communication range, TA will j Update v with the help of i Part of the private key.

[0108] The partial private key update phase: When the vehicle v i In the fog node FN j When requesting to update some private keys within the communication range, TA will j Update V with the help of i The specific steps are as follows:

[0109] Step 61: Vehicle v i Select random number As v i Part of the private key and calculate v i New partial public key Then, V i Get a new pseudonym Encrypted with session key sk and send Give fog node FN j ;

[0110] Step 62: FN j Decrypt with session key sk Get the corresponding plaintext and deliver Give TA to a trusted institution;

[0111] Step 63: After receiving FN j After sending the message, TA first selects a random number And calculate v i New partial public key and public key related information Then, TA is v i Calculate a new partial private key; by calculating the partial private key mod q, and finally TA uses h0(RID i )encryption Parameters and After obtaining and send To FN j ;

[0112] Step 64: FN j Send further Give vehicle v i , so far, v i Get the updated partial private key Then, get the new complete private key and the corresponding public key

[0113] like Figure 1 As shown, the present invention can be divided into five stages: system initialization, registration, mutual authentication and key negotiation, road condition monitoring, and partial private key update. The V2F authentication method is used to authenticate the legitimacy of the identities of both communicating parties and ensure the reliability of road condition messages.

[0114] Finally, defend against attacks. In the security model of the present invention, external attackers can monitor the communication channel between any two legitimate entities and capture all messages transmitted on the open communication channel. External attackers are curious about the identity of legitimate vehicles and the data they provide, and may launch various attacks (such as impersonation, replay, modification, and known session key attacks) to threaten the security of the road condition message transmission protocol based on fog computing. Therefore, the attack scenario mainly involves vehicle v i and fog node FN j The interaction between two entities over an insecure communication channel. Through several games between attacker A and challenger C, it is proved that attacker A cannot win the challenge initiated by challenger C with a non-negligible probability within polynomial time (PPT). Assume Denotes the K instances of entity U. A random query initiated by attacker A and challenger C’s response to attacker A’s query are shown below:

[0115] Extractvehicle(RID i ):When holding vehicle v i Real Identity RID i When attacker A initiates the query, challenger C runs the key generation algorithm and adds (a i , c i ), RID i To list L vki .

[0116] ExtractFogNode(ID FNj ): When holding fog node FN j ID fj Attacker A initiates this query, challenger C runs the key generation algorithm and adds sk j , ID fj To list L Fkj .

[0117] Attacker A who has message m initiates this query, and challenger C executes the designed protocol normally and sends the result to attacker A.

[0118] Execute(v i , FN j ): This query simulates the scenario where attacker A launches an eavesdropping attack. When challenger C outputs the actual operation of this protocol, vehicle v i and fog node FN j The messages exchanged between them.

[0119] After receiving the query from attacker A, challenger C sends vehicle v i and fog node FN j The session key negotiated between them is returned to A.

[0120] Corrupt vehicle (v i ): Attacker A obtains vehicle v through this query i The complete private key.

[0121] CorruptFogNode(FN j ): Attacker A obtains fog node FN through this query j The private key.

[0122] Attacker A uses v i Instance (which satisfies Definition 5.2) executes this query. Challenger C chooses b∈{0,1}, if b=1, Challenger C outputs Otherwise, the challenger C provides the attacker A with a random integer with the same bit length as the session key.

[0123] Related definitions:

[0124] Definition 1.1 (Partnership): If v i and FN j If two instances share the same partner identity, can authenticate each other, and share the same session key, then they are partners of each other.

[0125] Definition 1.2 (Freshness): If Neither the company nor its partners use the query Example Considered fresh.

[0126] Definition 1.3 (Semantic Security of Session Key): In the proposed security model, the attacker A’s task is to identify the real session key and random key of an instance. Attacker A can Execute a query At the end of their interactive game, attacker A needs to send a guess bit b' to challenger C. Let E represent an event where attacker A outputs a correct guess bit b' equal to the random bit b. Then the probability that attacker A breaks the semantic security of the session key of the present invention is expressed as: If any PPT attacker with negligible probability If the game is won, it can be concluded that the session key in the present invention is semantically secure.

[0127] The security proof of the proposed traffic information transmission protocol with privacy and reliability is as follows:

[0128] Theorem 1.1: If a PPT attacker A attempts to If we can break the designed protocol, we can construct a PPT attacker B to solve ECCDHP with probability ε. In addition, The relationship between and ε satisfies the following equation:

[0129]

[0130] Among them, q hi ,q s ,q e ,t are used to represent the maximum number of times an attacker queries the hash random oracle, send random oracle, random oracle, and polynomial time respectively.

[0131] Proof: Play several games G between attacker A and challenger C i , i∈(0,4), is used to show that the present invention is provably secure. Assume E i Represents the event that attacker A outputs the correct guess bit b′ equal to the random bit b.

[0132] Game G0: Attacker A attempts to break the present invention and issues multiple queries. Challenger C issues queries to Attacker A and responds accordingly as follows:

[0133] ExtTactQueTY: Based on the specific type of participants, ExtTactQueTY is divided into the following two sub-queries.

[0134] 1)Extactvehicle(RID i ): After receiving this query, attacker C checks whether there is already a record (RID i ,sk i =(a i , c i )). If it exists, C returns RID i Give attacker A; otherwise, C calculates A i =a i P, B i =b i P, c i =b i +s·h1(A i ||B i )mod q, and add (RID i ,sk i =(a i , c i )) and (RID i ,h1(A i ||B i )) to list L vki and list Finally, C sends RID i Give A.

[0135] 2)ExtTactFogNode(ID FNj ): After receiving this query, attacker C first checks the record ((ID fi ,sk j ) already exists. If it exists, C sends ID fi Give attacker A; otherwise, C calculates D j =d j and sk j =d j +s·h2(ID fj ||D j )mod q, and add (ID fi ,sk j ) and (ID fi ,h2(ID fj ||D j )) to list L Fkj and list Finally, C sends ID fi Give A.

[0136] sendQueTY: According to the specific types of participants and messages, the sendQueTY query can be divided into the following sub-queries.

[0137] 1)send( AuthenticationRequest): This query mainly simulates the vehicle v i To the nearby fog node FN j Send an authentication request, and attacker A can obtain v i The message sent m v1 After receiving this query, challenger C selects a random number x i , and calculate a i =h2(PID i ||B i ||x i ||T i ),σ i1 =c i +a i +x i a i mod q. Eventually, challenger C will return m v1 ={PID i , X i , A i , B i ,σ i1 ,T i}Give A.

[0138] 2)send( m v1 ): This query simulates the fog node FN j Response Vehicle v i In the authentication request scenario, attacker A can obtain FN j The message sent m f2 Upon receiving this query, the challenger C checks the signature σ i1 If it is correct, C calculates Y j =y j P, sk ji =h3(y j X i ||PID i ||ID fj ), β j1 =h2(sk ji ||Y j ||T j ) and σ j1 =sk j +β j1 yj modq, where Otherwise, C sends m f2 ={ID fj , Y j , D j ,σ j1 , T j} to attacker A.

[0139] 3)send( m f2 ): This query simulates the vehicle v i Verify fog node FN j The message sent m f2 After receiving the query, challenger C first checks β j1 Is it equal to h2(sk ij ||Y j ||T j ). If the two are equal, C calculates sk ij =h3(x i ·Y j ||PID i ||ID fj ); otherwise, C terminates the query.

[0140] Execute(v i , FN j ): Upon receiving this query, challenger C recovers (m v1 , m f2 ) and sends it to attacker A.

[0141] Reveal After receiving the query, challenger C returns the session key sk to attacker A.

[0142] CorruptQuery: According to the specific type of participants, CorruptQuery can be divided into the following two sub-queries.

[0143] 1)CorruptVehicle(v i ): In this query, challenger C sends vehicle v i The complete key sk i =(a i , c i ) to attacker A.

[0144] 2)CorruptFogNode(FN j ): In this query, the challenger C sends the fog node FN j The long-term key sk j Give the attacker A.

[0145] Test After receiving this query, the challenger C selects a random bit stream b∈{0,1}. If b=1, the selection appears in Reveal Otherwise, C randomly generates a random number with the same length as the session key and sends it to A.

[0146] Game G0 simulates the real attack of attacker A on the present invention. The probability of A breaking G0 is as described in Definition 1.3. Then we can know

[0147]

[0148] Game G1: This game simulates a hash cycle and L hi , i∈(0,3) is held by challenger C. After receiving the query, C checks (m, h i (m)) is already in the list L hi If it exists, return (m, h i (m)) to attacker A; otherwise, C will (m, h i (m)) is stored in list L hi , and send h i (m) to A, However, game G0 and game G1 are indistinguishable, so there is

[0149] Pr[E1]=Pr[E0] (1-9)

[0150] Game G2: When no collision occurs, the game is similar to G1. According to the birthday paradox, a hash function h i , the maximum probability of collision for i∈(0,3) is (q s +q e ) 2 / 2q as a random integer x i and y j The maximum probability of a collision between

[0151]

[0152] Game G3: If attacker A guesses the authenticator's value directly without querying the hash loop, then Game G2 will fail. Because it is difficult to distinguish between Game G2 and Game G3, there is

[0153]

[0154] Game G4: ECCDHP is used to execute Game G4. Select random number Then calculate x separatelyi =xP and y j =yP. From this we can get

[0155]

[0156] Combining equations (1-8) to (1-12), we can obtain the following equation:

[0157]

[0158] Through the above proof and analysis, the methods for resisting impersonation attacks, replay attacks, modification attacks and session attacks are summarized as follows:

[0159] Resisting impersonation attacks: On the one hand, to forge a legitimate fog node that sends a message, the attacker needs to forge a valid signature σ that satisfies equation (1-3) j1 , where σ j1 =sk j +h2(sk ji ||Y j ||T j )y j mod q. However sk j is the unique private key assigned to the legitimate fog node, y j It is a dynamically updated random number, and solving ECDLP is very difficult, so the attacker cannot forge a valid signature of a legitimate fog node σ j1 On the other hand, the present invention can resist the attack of impersonating the vehicle. According to Theorem 1.1, the attacker does not know the complete private key of the vehicle (a i ,c i ) and the dynamically updated random number x i In this case, it is impossible to forge a signature σ of a legitimate vehicle that convinces the fog node FN i1 =c i +a i +x i h2(PID i ||B i ||X i ||T i In summary, the present invention can resist vehicle impersonation attacks and fog node impersonation attacks.

[0160] Resisting replay attacks: Replay attacks are when an attacker intercepts messages transmitted by legitimate entities (vehicles and fog nodes FN) and resends them to destroy the secure communication between legitimate entities. In this protocol, the timestamp T i 、T j and t i The pseudonyms included in the vehicle Signature σ i1 =ci +a i +x i h2(PID i ||B i ||X i ||T i )mod q, signature σ j1 =sk j +h2(SK ji ||y j ||T j )y j mod q and road news M i =h(m i ||t i ) is used to ensure the freshness of each message. The receiver of the message can detect replay attacks by verifying the timestamp contained therein. i1 For example, the fog node can verify the equation T F -T i ≥ΔT to detect replay attacks, where T i and T F Representative signature σ i1 The sending time and receiving time of ΔT are fixed values ​​set by the system. Therefore, this protocol can resist replay attacks.

[0161] Resistance to Modification Attacks: According to Theorem 1.1, during the mutual authentication and key agreement phase of the present invention, if an attacker modifies any content in a message sent by a vehicle or fog node, the verifier can determine whether the message has been modified based on the vehicle and fog node signatures. Furthermore, an attacker cannot modify the road condition messages reported by vehicles during the road condition monitoring phase. This is because the road condition messages are encrypted with a session key, which is negotiated between the legitimate vehicle and the fog node. Therefore, an attacker cannot modify the road condition messages without knowing the session key SK. Therefore, the present invention is resistant to modification attacks.

[0162] Resistance to known session key attack: Since x i and y j It is a different random number selected by the vehicle and fog node during the mutual authentication and key negotiation phase, so each session key sk=h3(x i Y j ||PID i ||ID fj )=h3(y j x i ||PID i ||ID fj) are all different. This also means that an attacker cannot calculate the next session key from the current session key. Therefore, even if the current session key is leaked, it will not affect the security of other session keys.

[0163] To ensure the reliability of road messages, the FN is set to receive and process the message only when more than a threshold w different vehicles report the same road message. The threshold w is mainly obtained through experiments on the simulation platform designed in this section.

[0164] This section first constructs an experimental platform and performs simulations based on a real dataset to demonstrate the feasibility of the present invention and determine the system threshold w. The simulation platform used in this experiment is the Ubuntu 18.04 operating system. The code used for the simulation experiment has been attached to the GitHub project, the corresponding link is: https: / / github.com / PoisonousBelief / VANET_truth_discovery_

[0165] The simulation experiments use a real-world Point of Interest (POI) dataset from AutoNavi Maps, which collects POI data points (houses, shops, mailboxes, bus stops, etc.) from China from June 16, 2017, to August 13, 2017. Furthermore, to maintain the generality of this invention and ensure a relatively accurate determination of the system threshold w, this section primarily selects data points from three different cities (Guangzhou, Shanghai, and Shenzhen) for simulation experiments.

[0166] The P / R curve of the simulation experiment in this section is as follows: Figure 3 As shown, from Figure 3 As can be seen from the results in , all curves are close to the upper right corner of the figure, which shows that the present invention has a strong recognition ability. Figure 4 The relationship between precision and recall (P / R) and the threshold is described. When P / R is 1, the precision and recall are equal, indicating that the present invention has reached the break-even point (BEP), which also means that the precision and recall can be balanced as much as possible at this point. Therefore, the system threshold w can be accurately located at the BEP of the curve.

[0167] Figure 3 P / R curves of Guangzhou, Shanghai and Shenzhen (all P / R curves are close to the upper right of the figure, indicating that the present invention has strong recognition ability).

[0168] Figure 4P / R ratios of Guangzhou, Shanghai, and Shenzhen under different values ​​of w (the equilibrium point BEP is reached when P / R is 1, and the w corresponding to this point is set as the system threshold. Since the number of vehicles is an integer, the system threshold is rounded up).

[0169] The above is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several changes and improvements can be made without departing from the overall concept of the present invention, and these should also be regarded as the scope of protection of the present invention.

Claims

1. A reliable road condition information transmission method based on vehicle-mounted fog computing, characterized by: It includes registration phase, mutual authentication and key negotiation phase, road condition monitoring phase and partial private key update phase.

2. The reliable road condition information transmission method based on vehicle-mounted fog computing according to claim 1 is characterized in that: The registration phase refers to the need for vehicles and fog nodes FN to register with the TA before entering the road condition communication system. For vehicles, the TA will generate a partial private key for them during the vehicle registration phase, and the vehicle can generate a complete private key based on the partial private key. Unlike vehicles, a long-term public-private key pair is generated for the fog node FN at this stage. The registration phase is carried out over a secure communication channel. The registration of the vehicle and fog node FN is as follows: Vehicle Registration: Step 11: Get a real identity RID i Vehicle v i , choose a random number And calculate the vi part of the public key A i =a i p, where q and p are the order and generator of the additive cyclic group G based on the elliptic curve; Finally, the vehicle sends a <RIDi,A i > to him; Step 12: When TA receives vehicle v i When registering a request, a random number is selected And calculate the vi part of the public key B i =b i Partial private key information of p and vi Where h0 is the hash function SHA-2; then, according to the system master private key s, TA is the vehicle v i Generate a partial private key c i , c i =b i +s.h1(A i ll B i )mod q, h1 is the hash function SHA-2; Finally, TA sends <c i , C i >Will be preloaded into the vehicle's tamper-proof device and stored in a local database <RID i , c i >； Step 13: Vehicle v i Generate its complete private key (a i , c i ) and the corresponding public key (A i , B i ); Fog node FN registration: Step 21: j-th fog node FN j , submit your own identity IDR to TA to request registration; Step 22: Receive FN j The TA who registered the request selects a random number As FN j The private key is calculated; the corresponding public key D j =d j p and sk j =d j +s·h2(ID fj ||D j )mod q, h2 is the hash function SHA-2; then, TA converts the long-term public and private key pair <D j ,sk j >Send to FN j ; Step 23: FN j Secret Storage <D j ,sk j >.

3. The reliable road condition information transmission method based on vehicle-mounted fog computing according to claim 1 is characterized in that: In the mutual authentication and key negotiation phase, the vehicle and the fog node FN need to enter the mutual authentication and key negotiation phase; when the vehicle v i Enter FN j When the communication range is i and FN j Quickly complete mutual authentication and negotiate a session key for subsequent secure communication; i and FN j The detailed steps of mutual authentication and key agreement are shown below: Single authentication: Step 31: When the vehicle v i Enter FN j When the communication range is within, randomly select an integer And calculate X i =x i p; then v i Generate pseudonymous PID i ,Right now Where T i Indicates the current timestamp, p pub is the system public key, h3 is the hash function SHA-2; then v i Generate signature σ i1 , where σ i1 =c i +a i +x i a i mod q and a i =h2((PID i ||B i ||X i ||T i ); Finally, v i Will <PID i , X i , A i , B i ,σ i1 , T i >Send to FN j ; Step 32: After receiving v i Messages sent <PID i , X i , A i , B i ,σ i1 , T i >Followed by FN j First check the timestamp T i The timeliness of v i Time and FN of sending the message j The time of receiving the message is T i and T F , and use ΔT to represent the effective interval time of sending and receiving messages set by the system; if T F -T i ≥ΔT,FN j Discard the message; otherwise check the signature σ i1 The validity of , verify equation (1-1): s i1 p=B i +p pub h1(A i ||B i )+A i +x i a i (1-1) Among them: a i =h2(PID i ||B i ||X i ||T i ); The correctness of equation (1-1) is proved below: If equation (1-1) holds true, FN j Pick a random number And calculate Y j =y j p; then, FN j Generate and v i The session key sk ji , that is, sk ji =h3(y j X i ||PID i ||ID fj ); Finally, FN j Calculating β j1 =h2(sk ji ||Y j ||T j ), generate signature σ j1 =sk j +β j y j mod q, and send <ID fj , Y j , D j , σ j1 , T j > give v i , where T j is the current timestamp; Step 33: v i Receive FN j Messages sent <ID fj , Y j , D j ,σ j1 , T j >After, v i First check the timestamp T j The validity of j Invalid, v i The message will be discarded; otherwise v i Calculate the session key sk ij =h3(x i y j ||PID i ||ID fj ); Then, calculate β j1 =h2(sk ij ||Y j ||T j ) to check whether equation (1-3) is true; if not, reject the message; otherwise v i Receive the message; σ j1 p=D j +p pub h2(ID fj ||D j )+β j1 y j (1-3) The following is a detailed proof of the correctness of equation (5-3): So far, v i With FN j Successfully verified the legitimacy of each other's identities and negotiated a session key sk = sk ij =sk ji The session key sk negotiated in this process can be used to ensure v i and FN j Safety communication during the road condition monitoring phase; Batch certification: The fog node FN receives the pseudonyms {PID1, PID2, ..., PID k When a message is signed by k vehicles, batch authentication technology is used to aggregate k signatures into one signature. Subsequently, FN authenticates the single signature. Compared with authenticating k signatures separately, batch authentication technology is used to authenticate the signatures of k vehicles at one time, which greatly improves the authentication efficiency of FN. The details of FN batch authentication of k vehicle signatures are described as follows: Step 4 1: Fog node FN checks timestamp T i The validity of , where i∈(1, k); if T i If the valid time interval is exceeded, FN refuses to accept all k signatures; otherwise, FN continues to perform the following steps; Step 42: FN selects a vector u={u1,…,u i ,…,u k },u i Is FN from the interval [1, 2 t ] is randomly selected and t is a small integer; the computational cost of this process is negligible; Then, FN checks whether equation (1-5) holds; If equation (1-5) does not hold, it means that there are one or more invalid signatures among the signatures of these k vehicles. FN can use binary search technology to find all invalid signatures; otherwise, FN receives the signatures of these k vehicles and continues to execute the following steps; The correctness proof of equation (1-5) is shown below:

4. The reliable road condition information transmission method based on vehicle-mounted fog computing according to claim 1 is characterized in that: The road condition monitoring stage specifically includes: Step 51: When the vehicle v i Located at fog node FN j When reporting traffic information within the communication range, first calculate the traffic information M i＝ h(m i llt i ), h is the hash function, m i Indicates traffic information and t i represent Current timestamp; to ensure m i Confidentiality of data uploaded in public channels,Vehicle V i Encrypt m with the session key SK i Then use ES encryption operation to obtain C i =ES sk (m i llt i ll M i ); Finally, v i Send C i To FN j ; Step 52: Receive V i The C sent i After that, fog node FN j Execute the symmetric decryption algorithm to obtain C i The corresponding plain text traffic information (m i ll t i ll M i )=DS sk (C i ); Then, the fog node FN j Check timestamp t i Is it within the valid time interval, and through the equation h(m i ll t i )=M i Verify the integrity of the message; Step 53: FN j Only when at least w different vehicles report message m i , the traffic condition message will be received and processed, where w is the threshold determined by the system; otherwise, FN j Refuse to receive m i .

5. The reliable road condition information transmission method based on vehicle-mounted fog computing according to claim 1 is characterized in that: The partial private key update phase: When the vehicle V i In the fog node FN j When requesting to update some private keys within the communication range, TA will j Update V with the help of i The specific steps are as follows: Step 61: Vehicle v i Select random number As v i Part of the private key and calculate v i New partial public key Then, V i Get a new pseudonym Encrypted with session key sk and send Give fog node FN j ; Step 62: FN j Decrypt with session key sk Get the corresponding plaintext and deliver Give TA to a trusted institution; Step 63: After receiving FN j After sending the message, TA first selects a random number And calculate v i New partial public key and public key related information Then, TA is v i Calculate a new partial private key; by calculating the partial private key modq, and finally TA uses h0(RID i )encryption Parameters and Afterwards, and send To FN j ; Step 64: FN j Send further Give vehicle v i , so far, v i Get the updated partial private key Then, get the new complete private key and the corresponding public key

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