An efficient and multi-dimensional privacy protection vehicle formation method and system

Abstract

The present invention discloses an efficient, multi-dimensional privacy-protected vehicle formation method and system, the system includes a trusted agency TA, which is responsible for generating system public parameters, distributing keys, and generating and regularly updating pseudonyms and reputation values; a service provider SP, which is responsible for maintaining a pseudonym-reputation-attribute list, and selecting a pilot vehicle and matching follower vehicles to form a vehicle formation; a roadside unit RSU, which is used to promote communication between the vehicle and the trusted agency TA and the service provider SP. With the assistance of the service provider, the present invention realizes efficient vehicle formation based on the location, attributes and reputation value of the vehicle, and can ensure that the identity privacy, location privacy, attribute privacy and reputation value privacy of the vehicle are not leaked under low computing and communication overhead, thereby providing multi-dimensional privacy protection and significantly improving the reliability and security of formation formation.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking security technology, and in particular to a method and system for forming a vehicle formation with high efficiency and multi-dimensional privacy protection. Background Art

[0002] In recent years, the congestion in cities and highways caused by the sharp increase in the number of cars has caused people to worry about resource waste, traffic safety and environmental pollution. Pollutant emissions caused by traffic congestion are the main cause of environmental degradation and urban smog. Therefore, new architectures need to be developed to improve the performance and efficiency of the Internet of Vehicles.

[0003] As an important technology to solve the above problems, vehicle platooning has attracted widespread attention worldwide. A vehicle platoon consists of a pilot vehicle and several follower vehicles, which follow the pilot vehicle in a specific order. The pilot vehicle issues instructions through vehicle-to-vehicle (V2V) communication to control the movement of the entire vehicle platoon. Vehicle platooning can maintain a short distance and a stable speed, and reduce fuel consumption and exhaust emissions by reducing air resistance, significantly improving traffic and energy efficiency. However, due to the openness and high dynamics of the Internet of Vehicles, existing vehicle platooning solutions face challenges in efficiency and privacy. How to achieve safe vehicle platooning and ensure the privacy of platoon vehicles, while effectively reducing the computing and communication overhead of key entities, is the primary problem that needs to be solved in vehicle platooning in the Internet of Vehicles.

[0004] In order to further enhance privacy protection in the Internet of Vehicles and address the serious privacy threats encountered by vehicles, Cheng et al. [Hongyuan Cheng, Mohammad Shojafar, Mamoun Alazab, et al. PPVF: Privacy-Preserving Protocol for Vehicle Feedback in Cloud-Assisted VANET[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 9391-9403.] proposed a privacy-preserving protocol for vehicle feedback assisted by a service provider. In this protocol, with the assistance of the roadside unit, the service provider obtains the total number and related parameters of the vehicle through feedback to calculate the reputation value of the target vehicle and protects the privacy of the vehicle feedback to a certain extent. However, an honest and curious service provider may still infer the reputation value of the vehicle through the feedback information, threatening the privacy of the reputation value.

[0005] In order to solve the problem of large overhead of vehicle platoons, Li et al. [Runchuan Li, Zhiquan Liu, YongMa, et al. RPPM: A Reputation-Based and Privacy-Preserving Platoon Management Scheme in Vehicular Networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 25(6): 6147-6160.] designed a secure comparison protocol to select a leader vehicle for each vehicle platoon. In this protocol, the service provider aggregates the reputation ciphertext and feedback score of the leader vehicle, and retains the privacy of the leader vehicle's reputation value without affecting the aggregation result. However, in the "honest and curious" service provider scenario, the attacker may infer the reputation value of the vehicle through the aggregation operation, thereby threatening the reputation privacy. Cheng et al. [Hongyuan Cheng, Xianchao Zhang, Jingkang Yang, et al. PPRT: PrivacyPreserving and Reliable Trust-Aware Platoon Recommendation Scheme in IoV[J].IEEE Systems Journal, 2023, 17(3): 4922-4933.] designed a privacy-preserving and reliable trust-aware vehicle platoon recommendation scheme, whose feedback report collection process does not leak the privacy and weight information of the following vehicles. Although the scheme protects the privacy of vehicles during the feedback process, it does not involve the location privacy and attribute privacy of vehicles, resulting in the risk of information leakage in the platoon formation stage. Summary of the invention

[0006] In order to solve the problems that the existing technology does not consider the privacy of vehicle location, attribute privacy and excessive vehicle formation overhead, the present invention provides an efficient and multi-dimensional privacy protection vehicle formation method and system.

[0007] To achieve the above object, the technical solution of the present invention is:

[0008] In a first aspect, the present invention provides an efficient, multi-dimensional privacy protection vehicle formation system, comprising:

[0009] The trusted authority TA is responsible for generating system public parameters, distributing keys, and generating and regularly updating pseudonyms and reputation values;

[0010] The service provider SP is responsible for maintaining the pseudonym-reputation-attribute list and selecting the lead vehicle and matching the follower vehicle to form a vehicle formation;

[0011] Roadside unit RSU is used to facilitate communication between vehicles and trusted institutions TA and service providers SP.

[0012] Furthermore, there are two service providers SP, namely SPA and SPB. The SPA is responsible for maintaining a pseudonym-reputation-attribute list and selecting a lead vehicle and matching follower vehicles. SPB assists SPA in completing the formation of a vehicle formation.

[0013] In a second aspect, the present invention provides an efficient, multi-dimensional privacy-protected vehicle formation method, based on the above system, the method comprising:

[0014] S1, system initialization, the trusted agency TA generates system parameters;

[0015] S2, vehicle registration, the vehicle registers with the trusted agency TA and obtains registration information;

[0016] S3, platoon driving request, the vehicle generates a platoon driving request and submits it to the service provider SPA;

[0017] S4: pilot car selection: service provider SPA and service provider SPB collaborate to select a pilot car;

[0018] S5: matching of following vehicles: service provider SPA and service provider SPB cooperate to match following vehicles;

[0019] S6. Feedback report submission: the pilot vehicle aggregates the feedback reports of the vehicle formation and submits them to the trusted institution TA;

[0020] S7, reputation and pseudonym update, the trusted agency TA processes the feedback report and updates the reputation and pseudonym of the vehicle.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1) The present invention distributes keys to vehicles and service providers through a trusted institution TA, generates pseudonyms and reputation values ​​for vehicle registration, and with the assistance of service providers, realizes efficient vehicle formation based on the location, attributes and reputation values ​​of the vehicles, ensuring that the identity privacy, location privacy, attribute privacy and reputation value privacy of the vehicles are not leaked, thereby providing multi-dimensional privacy protection and significantly improving the reliability and security of formation.

[0023] 2) This invention designs a secure reputation value ciphertext comparison protocol, combined with the Paillier encryption algorithm, to ensure that the reputation value always exists in ciphertext form during the formation of the vehicle formation. Even an "honest and curious" service provider cannot infer the specific reputation value, thereby effectively protecting the privacy of the reputation value and overcoming the risk of reputation value privacy leakage in the existing solution. At the same time, this design can safely select the vehicle with the highest reputation value as the pilot vehicle, improving the reliability of the vehicle formation.

[0024] 3) The present invention designs an attribute verification and matching algorithm, uses bilinear pairing and BLS signature to verify the integrity of attribute values, and implements attribute matching through the privacy set intersection technology based on Bloom filters to prevent the leakage or tampering of attribute values ​​during the verification and matching process. The algorithm supports batch verification, greatly reduces the computation and communication overhead, improves the efficiency and security of the system, and solves the vehicle attribute privacy problem that has not been addressed in the prior art.

[0025] 4) The present invention protects the privacy of vehicle identity through pseudonym technology and Anonymity and Hadamard product technology effectively protect the vehicle location privacy and further enhance the privacy protection capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the composition of an efficient, multi-dimensional privacy-protected vehicle formation system provided by an embodiment of the present invention;

[0027] Figure 2 A flowchart of an efficient, multi-dimensional privacy-protected vehicle formation method provided by an embodiment of the present invention;

[0028] Figure 3 To compare the computational overhead of different technical solutions in the comparative experiment;

[0029] Figure 4 To compare the communication overhead of different technical solutions in the comparative experiment;

[0030] Figure 5 Comparison of the computation and communication overhead of one-by-one verification and batch verification on the SP side when the number of vehicles changes from 2 to 40;

[0031] Figure 6 Comparison of the computational overhead of each entity in different schemes when the number of vehicles is 10, 20, 30, and 40;

[0032] Figure 7 Comparison of the communication overhead of each entity in different schemes when the number of vehicles is 10, 20, 30 and 40 respectively. DETAILED DESCRIPTION

[0033] Example:

[0034] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0035] See also Figure 1 This embodiment provides an efficient, multi-dimensional privacy protection vehicle formation system, and the entities involved mainly include a trusted organization, two service providers, several roadside units and a large number of vehicles.

[0036] The Trusted Authority (TA) is mainly responsible for generating system public parameters, distributing keys to vehicles and service providers, generating pseudonyms and reputation values ​​for vehicle registration, and updating these values ​​regularly. The two service providers (SP), denoted as SPA and SPB, have sufficient computing and storage capabilities. Among them, SPA is responsible for maintaining a pseudonym-reputation-attribute list, selecting a pilot vehicle and matching follower vehicles, and SPB assists SPA in completing the formation of a vehicle formation. The roadside unit (RSU) acts as a relay node to facilitate communication between vehicles and TA and SP. The vehicle is equipped with a trusted platform module to communicate with nearby roadside units RSU and other vehicles to exchange information. After joining the vehicle formation, the vehicle acts as a pilot vehicle or a follower vehicle according to its role identifier.

[0037] In addition, TA is considered to be a fully trusted entity (i.e., it can honestly execute system instructions and fully protect the privacy of the vehicle), SP and RSU are considered to be semi-trusted entities (i.e., honest and curious, that is, they can honestly execute system instructions but may reveal the privacy of the vehicle from known information), and the vehicle is considered to be an untrusted entity (i.e., it may maliciously execute system instructions and may also reveal the privacy of other vehicles).

[0038] It can be seen that the efficient, multi-dimensional privacy-protected vehicle formation system provided by this embodiment distributes keys to vehicles and service providers through the trusted agency TA, generates pseudonyms and reputation values ​​for vehicle registration, and with the assistance of service providers, based on the vehicle's location, attributes and reputation value, realizes efficient vehicle formation to ensure that the vehicle's identity privacy, location privacy, attribute privacy and reputation value privacy are not leaked, thereby providing multi-dimensional privacy protection and significantly improving the reliability and security of formation.

[0039] This embodiment provides an efficient, multi-dimensional privacy protection vehicle formation method. Figure 2 As shown, it mainly includes the following steps:

[0040] S1, system initialization, the trusted agency TA generates system parameters;

[0041] S2, vehicle registration, the vehicle registers with the trusted agency TA and obtains registration information;

[0042] S3, platoon driving request, the vehicle generates a platoon driving request and submits it to the service provider SPA;

[0043] S4: pilot car selection: service provider SPA and service provider SPB collaborate to select a pilot car;

[0044] S5: matching of following vehicles: service provider SPA and service provider SPB cooperate to match following vehicles;

[0045] S6. Feedback report submission: the pilot vehicle aggregates the feedback reports of the vehicle formation and submits them to the trusted institution TA;

[0046] S7, reputation and pseudonym update, the trusted agency TA processes the feedback report and updates the reputation and pseudonym of the vehicle.

[0047] In a specific embodiment, the above step S1 includes:

[0048] First, assume that the security parameter of the system is TA defines two cyclic groups and , and select Random elements ,in yes The generator of is prime number order Then, define the bilinear map , and choose two public hash functions and . Then, TA randomly selects As the master private key, calculate the master public key , and then initialize two key pairs, namely asymmetric key pairs and homomorphic key pairs Finally, TA broadcasts the public parameters , and transmit it via a secure wired link Send to SPA, Send to SPB.

[0049] In a specific embodiment, the above step S2 includes:

[0050] When a vehicle In time interval When registering with the Internet of Vehicles (i.e. applying for a pseudonym and other communication information), TA first assigns it a real identity , and then generate a pseudonym for it ,in .

[0051] Next, TA initializes Property value , and Assign a attribute set with attribute values ,in At the same time, TA constructs a Bloom filter , and use random values and Separate hash functions Initialize all elements to zero. The hash function The range is Then, he Time setting The reputation value is , and limited to the range In which (Since the plaintext in the Paillier algorithm ranges from 0 to , and the modulus is , so use Used to indicate ). Then, TA uses Generate reputation ciphertext , and locally stores and maintains a pseudonym-reputation list. Finally, TA randomly selects an integer , and calculate , then (and its components )and Stored in Equipped with TPM (Trusted Platform Module).

[0052] Since the hash function used to construct the pseudonym is irreversible and collision-resistant, even if a malicious attacker is curious about the real identity of the vehicle and obtains the pseudonym of the vehicle, , and it is impossible to reversely infer the true identity of the vehicle In addition, the same vehicle uses different pseudonyms at different time periods, which makes it impossible for attackers to track and link vehicles over a long period of time. Therefore, the pseudonym technology based on this step can effectively protect the identity privacy of the vehicle.

[0053] In a specific embodiment, the above step S3 includes:

[0054] when When there is a platoon driving request, it first Generate a symmetric key for each attribute in ,in , and then calculate the attribute value ciphertext , and split it into sectors, represented by .then, Random Selection As its attribute master private key, and calculate as its attribute master public key. Then, it generates ,in will be sent to SPA as an attribute tag. All are divided into sectors , it is difficult for malicious attackers to reconstruct In addition, based on the irreversibility and anti-collision properties of hash functions and the discrete logarithm problem, Extract It is computationally infeasible, thus effectively protecting the attribute privacy of the vehicle.

[0055] at the same time, Convert your location to Asterisk The location point is then encrypted with the attribute value Fill in each asterisk to generate a position attribute matrix .also, Setting up a Bloom filter , , insert each attribute value into the Bloom filter. Using anonymous technology, malicious attackers try to obtain When extracting the vehicle’s location information, the specific details of the vehicle’s location cannot be determined, thereby effectively protecting the vehicle’s location privacy.

[0056] In a convoy of vehicles, By role identifier Serve as a follower or pilot vehicle. Specifically, express Serve as a follower car, express Serve as the pilot car. use encryption Get the role identifier ciphertext , and construct a platoon driving request .Then, Random Selection And calculate , and .at last, Through RSU Sent to SPA to request platoon driving.

[0057] After receiving the platoon driving request, SPA first Extract , and derive the current time interval from its clock , by checking whether To verify Then, SPA checks the equation Is it established to verify The validity of this equation is proved as follows:

[0058]

[0059] If the verification is successful, SPA considers Valid, otherwise SPA discards it; then, SPA uses right Decrypt it to get .based on ,SPA securely stores the pseudonyms, reputation values, and attribute information of vehicles that have submitted platoon driving requests in a pseudonym-reputation-attribute list.

[0060] In a specific embodiment, the above step S4 includes:

[0061] The local clock records the arrive In the time interval (where and is a continuous time interval), SPA extracts two pseudonym sets from the pseudonym-reputation-attribute list. Without loss of generality, we assume that each (indicates following car), each (denoting the pilot vehicle), where as well as . and Respectively represent the number of vehicles seeking to be the leader and follower in platoon driving. When the SP executes the security reputation value ciphertext comparison protocol, select The vehicle with the highest reputation is recorded as , and designate it as the pilot vehicle. The protocol requires SPA to execute the first algorithm and SPB to cooperate in executing the second algorithm; the detailed description is as follows:

[0062] SPA executes the first algorithm and converts The first car in Set to the highest reputation , and encrypt its reputation Set to SPA then encrypts 1 to , and Each vehicle in to )calculate and . Next, the SPA flips a coin , and choose a random number Make .if , then SPA calculates ,if , then SPA calculates . Then, the SPA will Sent to SPB for decryption and judgment, and received from SPB .if , then SPA will Set to and will Update to new Finally, the SPA returns As a pilot car.

[0063] Upon receiving from SPA After that, SPB executes the second algorithm, using right Decrypt it and get . Subsequently, SPB judged Is it satisfied and will It is returned as output to SPA.

[0064]

[0065]

[0066] In the first algorithm, based on lines 5 to 6, we can derive and In the second algorithm, if , which means ,and is a negative number. , can be deduced from equation (1) ,Right now ;otherwise, is a positive number, that is In this step, SPA cannot obtain the specific reputation value because it lacks the Paillier private key; at the same time, SPB can only decrypt the converted reputation value ciphertext, so it cannot obtain the specific reputation value of the vehicle. Through the secure reputation value ciphertext comparison protocol, the vehicle with the highest reputation value can be effectively selected as the pilot vehicle, while protecting the privacy of the reputation value from malicious infringement.

[0067]

[0068] In a specific embodiment, the above step S5 includes:

[0069] For ease of illustration, we will focus on the formation of a single vehicle formation, designating the vehicle with the highest reputation as the lead vehicle. , and The leading vehicle formation is named Without loss of generality, we will satisfy the vehicle formation The following vehicles required to join the team are represented as a set .

[0070] The SPA first retrieves some information from the pseudonym-reputation-attribute list, including Vehicle as well as of and SPA then executes the attribute verification matching algorithm (i.e., the third algorithm) to evaluate each following vehicle to verify whether they meet the vehicle formation. The entry requirements (i.e. position requirements and attribute requirements) of .

[0071]

[0072] In order to resist attribute tampering attacks during the attribute verification process, we use BLS signature and challenge-response model. When there is a need for attribute verification, SPB randomly selects an index set , and request an integrity audit certificate from SPA. To ensure randomness, a random coefficient is introduced into each attribute value. SPB will query Send to SPA, SPA according to The proof is generated and returned to SPB for integrity audit of the attribute value. The detailed description of the attribute verification matching algorithm is as follows:

[0073] SPA first initializes an empty collection , used to store vehicles that meet the requirements for joining the team, and then SPA traverses Each vehicle in to ) to determine whether it meets the requirements for joining the team. SPA first checks the current vehicle The location attribute matrix With pilot car The location attribute matrix Is the result of the Hadamard product a non-zero matrix, that is . If this condition is met, it means Meet vehicle platooning However, the Hadamard product cannot directly determine the validity of the vehicle attribute value, so SPB is entrusted to perform integrity audit on the attribute value. SPB generates a set of random challenge index coefficients , and send it to SPA. Received After that, SPA first starts with Extract , then calculate the label proof And data prove SPA will then audit the Sent to SPB, SPB obtains it through a secure channel Sent , and by checking the equation Whether it is established to verify the integrity of the attribute value. The correctness of this equation is proved as follows:

[0074]

[0075] If the equation holds true, SPB will Set to 1 and Return to SPA. If , the integrity and authenticity of the attribute value are ensured. Next, based on the intersection of the privacy sets based on the Bloom filter, SPA and Performing bitwise AND operation, we get Then, calculate The number of 1s in the If this condition is met, it means Meet vehicle platooning , then Add to collection Finally, the SPA returns the collection , which contains all vehicles that satisfy the vehicle formation The following vehicle required to join the team.

[0076] It can be seen that in this step, attribute verification and matching are performed in ciphertext form, which effectively prevents the leakage of attribute privacy and therefore can effectively protect the attribute privacy of the vehicle.

[0077] In addition to the above-mentioned one-by-one attribute verification, SPB also supports batch verification of multiple attribute values. Using bilinear aggregate signatures, multiple signatures can be aggregated into one signature, thereby improving verification efficiency while maintaining verifiability. For ease of expression, we define , represents the number of vehicles to be verified in batches. SPA generates data proof And label proof , and aggregate the latter into SPA will then Sent to SPB, SPB passes the test equation Whether it is established is verified in batches. The correctness of this equation is proved as follows:

[0078]

[0079] At the end of this phase, the vehicle formation has been completed The composition consists of a pilot car and It consists of several following vehicles, among which express The number of vehicles following the vehicle.

[0080] In a specific embodiment, the above step S6 includes:

[0081] The update of reputation value depends on the feedback reports collected during the platoon driving process, where the follower car generates feedback reports for the lead car, and the lead car generates feedback reports for each follower car. The feedback report set is aggregated by the pilot car into ,in and Respectively The number of positive feedback reports and the number of negative feedback reports; the pilot car uses Encrypt feedback reports as Then, the pilot car randomly selects And calculate and The pilot car sends TA periodically via RSU. To update your reputation.

[0082] In a specific embodiment, the above step S7 includes:

[0083] TA received After receiving the feedback report, the current time interval is first derived from its clock , by checking whether To verify Then, TA checks the equation Is it established to verify The validity of this equation is proved as follows:

[0084]

[0085] If the verification is successful, TA considers Valid, otherwise TA discards it. Then, TA uses right Decrypt it and get , and extract Each vehicle and Then, TA is The vehicle in the update next time interval Reputation ,in Indicates the number of feedback reports in the feedback report set. and They are the positive feedback coefficient and negative feedback coefficient respectively, and their value range is 0~1.

[0086] In addition, TA will also The vehicle in the update next time interval Specifically, TA chooses a random value ， for Generate a new pseudonym . Then, TA calculates the new reputation value ciphertext , updates its locally stored pseudonym-reputation list.

[0087] In order to verify the effectiveness of the technical solution of the present invention, this embodiment also provides some comparative experiments:

[0088] The experiments were conducted on a laptop equipped with an AMD Ryzen 95900 HS eight-core processor, 16 GB of memory and a 64-bit Windows 11 operating system to show the running time and associated costs of each entity. The cryptographic operations were simulated using the Java programming language and the pairing-based Java Cryptography (JPBC) library, as shown in the following table:

[0089]

[0090] set up and The bit lengths are , and At the same time, set the time interval, hash value, symmetric encryption ciphertext, asymmetric encryption ciphertext and query The bit lengths are and byte.

[0091] Specifically, the technical solution of the present invention is compared with the existing BSPC scheme [Yanan Zhao, Yunpeng Wang, Yuhao Liang, et al. Identity-based broadcast signcryption scheme for vehicular platoon communication [J]. IEEE Transactions on Industrial Informatics, 2022, 19 (6): 7814-7824.], SPSC scheme [Chengzhe Lai, Guanjie Li, DongZheng. SPSC: A secure and privacy-preserving autonomous platoon setup and communication scheme [J]. Transactions on Emerging Telecommunications Technologies, 2021, 32 (9): e3982.], PPRT scheme [Hongyuan Cheng, Xianchao Zhang, Jingkang Yang, et al. PPRT: Privacy Preserving and Reliable Trust-Aware Platoon Recommendation Scheme in IoV [J]. IEEE Systems Journal, 2023, 17 (3): 4922-4933.] and TPPR scheme [Chuan Zhang, Liehuang Zhu, Chang Xu, et al. TPPR: Atrust-based and privacy-preserving platoon recommendation scheme in VANET[J].IEEE Transactions on Services Computing, 2019, 15(2): 806-818.] were compared. The detailed comparison results are as follows Figure 3-7 The results show that the technical solution proposed in the present invention has considerable computational and communication overheads compared with other solutions, greatly reduces computational and communication overheads, improves system efficiency and security, and solves problems such as vehicle location privacy and attribute privacy that are not addressed in the prior art.

[0092] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. An efficient, multi-dimensional privacy protection vehicle formation method, based on an efficient, multi-dimensional privacy protection vehicle formation system, the system includes: The trusted authority TA is responsible for generating system public parameters, distributing keys, and generating and regularly updating pseudonyms and reputation values; The service provider SP is responsible for maintaining the pseudonym-reputation-attribute list and selecting the lead vehicle and matching the follower vehicle to form a vehicle formation; Roadside unit RSU, used to facilitate communication between vehicles and trusted institutions TA and service providers SP; There are two service providers SP, namely SPA and SPB. SPA is responsible for maintaining a pseudonym-reputation-attribute list and selecting a lead vehicle and matching follower vehicles. SPB assists SPA in forming a vehicle formation. Characterized in that the method comprises: S1, system initialization, the trusted agency TA generates system parameters; S2, vehicle registration, the vehicle registers with the trusted agency TA and obtains registration information; S3, platoon driving request, the vehicle generates a platoon driving request and submits it to the service provider SPA; S4: pilot car selection: service provider SPA and service provider SPB collaborate to select a pilot car; S5: matching of following vehicles: service provider SPA and service provider SPB cooperate to match following vehicles; S6. Feedback report submission: the pilot vehicle aggregates the feedback reports of the vehicle formation and submits them to the trusted institution TA; S7, reputation value and pseudonym update, the trusted agency TA processes the feedback report and updates the reputation value and pseudonym of the vehicle; The step S1 comprises: Assume that the security parameter of the system is λ, and the trusted authority TA defines two cyclic groups: and and select s+1 random elements where g is The generator of , the prime order is q, s is a natural number; The trusted authority TA defines a bilinear mapping And choose two common hash functions H1:{0,1} * →{0,1} λ and Trusted institution TA randomly selects As the system master private key msk, calculate the system master public key mpk = g u , and initialize two key pairs, namely asymmetric key pairs (pk a ,sk a ) and homomorphic key pair (pk p ,sk p ); Trusted authority TA broadcasts public parameters And the private key sk of the asymmetric key pair a Send it to the service provider SPA and send the private key sk of the homomorphic key pair p Sent to the service provider SPB; The step S2 comprises: When a vehicle V i At time interval T k When registering with the inbound vehicle network, the trusted institution TA assigns a real identity to it Then generate a pseudonym for it The random value Trusted Agency TA Initialization Property value And for vehicle V i Assign an attribute set A containing n attribute values i ={a i,1 ,a i,2 ,…,a i,n },in At the same time, the trusted institution TA constructs a Bloom filter and use a random value σ i and t independent hash functions h1,h2,…,h t Initialize all elements to zero; Trusted institution TA in T κ Set the vehicle V i The reputation value is and is limited to the range [-R1, R1], where N is the modulus of the Paillier algorithm; The trusted authority TA uses the public key pk of the homomorphic key pair p Generate reputation ciphertext and locally store and maintain a pseudonym-reputation list; The trusted authority TA randomly selects an integer And calculate y = g x mod p; The trusted institution TA will i , and Stored in vehicle V i Equipped with Trusted Platform Module TPM; The step S3 comprises: When the vehicle V i When there is a platoon driving request, vehicle V i First, A i Each attribute value in generates a symmetric key {ak i,j } 1≤j≤n , where ak i,j =H1(a i,j ), and then calculate the attribute value ciphertext And divide it into s sectors, expressed as The kth shard of Vehicle V i Random Selection As its attribute master private key, and calculate As its attribute master public key, generate Among them ({ζ i,j } 1≤j≤n ,θ i ) will be sent to the service provider SPA as an attribute tag; at the same time, the vehicle V i Convert your location to The location points are indicated by an asterisk (*), and then the attribute value ciphertext is used to split the fragment Fill in each asterisk to generate a position attribute matrix Vehicle V i Setting up a Bloom filter Encrypt each attribute value Insert into Bloom filter; In a vehicle formation, vehicle V i According to the role identifier ack i ∈{0,1} acts as a follower or a pilot vehicle; vehicle V i Use the public key pk of an asymmetric key pair a Encrypted role identifier ack i Get the role identifier ciphertext sac i =Enc a (ack i ) and construct a platoon driving request Vehicle V i Random Selection And calculate and b i =r i +c i x(mod q), where a i ,c i and b i For verifying platoon driving requests Parameters of validity; Vehicle V i Through the road side unit RSU Send to the service provider SPA to request platoon driving; The service provider SPA receives a platoon driving request Then, T is extracted from κ , and derive the current time interval T′ from its clock κ , by checking whether T κ =T′ κ To verify the platoon driving request Freshness; The service provider SPA checks the equation Is it established to verify the formation driving request If the verification is successful, the service provider SPA considers the platoon driving request Valid, otherwise the service provider SPA discards it; The service provider SPA uses the private key sk of the asymmetric key pair a The ciphertext sac of the role identifier i Decrypt and get ack i =Dec a (sac i ); Based on ack i ,The service provider SPA securely stores the pseudonyms, reputation values, and attribute information of the vehicles that have submitted platoon driving requests in a pseudonym-reputation-attribute list.

2. The efficient, multi-dimensional privacy-protected vehicle formation method according to claim 1, characterized in that: The step S4 comprises: The local clock records the s to T e The time interval, where T s and T e is the consecutive time intervals at which the service provider SPA extracts two pseudonym sets from the pseudonym-reputation-attribute list; Assume that each Indicates the following car, each represents the pilot vehicle, where as well as and|I s→e | respectively represent the number of vehicles seeking to be the leader and follower in platoon driving; When T e -T s ≥T thr When the service provider SP executes the security reputation value ciphertext comparison protocol, select The vehicle with the highest reputation is denoted as V max and designate it as the pilot vehicle; the protocol requires the service provider SPA to execute the first algorithm and the service provider SPB to collaboratively execute the second algorithm; The service provider SPA executes a first algorithm, comprising: The service provider SPA will The first car V1 in the game is set to the car with the highest reputation value V max , and encrypt its reputation Set to Then the service provider SPA encrypts 1 into CR (1) , and Each vehicle in the calculate and Service provider SPA flips a coin j , and choose a random number R j Make If s j =1, the service provider SPA calculation If s j = -1, the service provider SPA calculation The service provider SPA will CR j,dif Send to SPB for decryption and judgment, and receive f from service provider SPB j ; if f j ·s j =1, the service provider SPA will vehicle V j Set to V max and will Update to new The service provider SPA returns the vehicle V max As a pilot car; The service provider SPB cooperates to execute a second algorithm, including: The service provider SPB uses the private key sk of the homomorphic key pair p CR j,dif Decrypt and get RV j,dif ; Service provider SPB judgment Is it satisfied, and f j It is returned as output to the service provider SPA.

3. The efficient, multi-dimensional privacy-protected vehicle formation method according to claim 2, characterized in that: The step S5 comprises: Designate the vehicle with the highest reputation as the pilot vehicle V j , and V j The leading vehicle formation is named Will satisfy vehicle formation The following vehicles required to join the team are represented as a set The service provider SPA retrieves information from the pseudonym-reputation-attribute list; subsequently, the service provider SPA performs an attribute verification matching algorithm to evaluate each following vehicle to verify whether they meet the vehicle formation Requirements for joining the team; When there is a need for attribute verification, the service provider SPB randomly selects an index set {j} 1≤j≤n and request an integrity audit certificate from the service provider SPA; Each attribute value introduces a random coefficient {v j } 1≤j≤n ; Service Provider SPB will query Sent to the service provider SPA, the service provider SPA A proof is generated and returned to the service provider SPB for integrity audit of the attribute value.

4. The efficient, multi-dimensional privacy-protected vehicle formation method according to claim 3, characterized in that: The step S6 comprises: Vehicle formation Feedback reports are aggregated by the pilot car into Where m i and n i Respectively represent T κ The number of positive feedback reports and the number of negative feedback reports; The pilot car uses the system master public key mpk to encrypt the feedback report into Pilot car random selection And calculate and b j =r j +c j x(mod q); The pilot vehicle sends a regular message to the trusted authority TA through the roadside unit RSU. To update your reputation.

5. The efficient, multi-dimensional privacy-protected vehicle formation method according to claim 4, characterized in that: The step S7 comprises: The trusted institution TA receives the vehicle formation After receiving the feedback report, the current time interval T′ is derived from its clock κ , by checking whether T κ =T′ κ To verify Freshness; The trusted authority TA checks the equation Is it established to verify If the verification is successful, the trusted authority TA believes that Valid, otherwise the trusted authority TA discards it; The trusted institution TA uses the system master private key msk to Decrypt it and get and extract m per vehicle i and n i ; Trusted institution TA is The vehicles in the update next time interval T κ+1 Reputation in represents the number of feedback reports in the feedback report set, ψ and ω are the positive feedback coefficient and negative feedback coefficient respectively.

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