A V2G electric vehicle privacy protection method, system, device and medium

Abstract

The application discloses a V2G electric vehicle privacy protection method, system, device and medium, comprising: considering the possible privacy leakage problem in the V2G charging scene of the electric vehicle, proposing to use pseudonym technology instead of real identity to prevent tracking. Then, the cross-chain technology is integrated into the V2G electric vehicle privacy protection framework of the application, and the decentralized architecture is conducive to safe pseudonym distribution and revocation. In addition, the application proposes an analysis index named privacy entropy to evaluate the privacy protection degree after the vehicle pseudonym is changed. By combining privacy entropy with inventory theory, the application proposes an optimization problem of pseudonym generation in V2G pseudonym protection, and adopts an algorithm based on reinforcement learning technology to realize efficient and economical pseudonym generation. Thus, the performance and time delay problems of the existing single-chain block chain system in processing a large number of pseudonym transactions are solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of automobile privacy protection, and in particular to a V2G electric vehicle privacy protection method, system, device and medium. BACKGROUND

[0002] An electric vehicle realizes interaction with a power grid through a V2G (Vehicle-to-Grid) technology, can be charged at a low power demand peak period, and can feed back power to the power grid at a peak period to optimize power grid load distribution, but in a V2G charging process, the electric vehicle needs to send information including vehicle identity information, a state of charge (SOC) of a battery, a maximum allowed charging and discharging current and the like to a charging pile, and a charging and discharging service provider can analyze private information such as charging preferences, a home address and social relationships of a user through acquired V2G charging data, and exchange of the information involves a demand for privacy protection. In order to solve the above problems, a vehicle can use a pseudonym instead of a real identity in communication, and the pseudonym is changed regularly to avoid being tracked continuously, so as to protect a location privacy of the vehicle and prevent leakage of sensitive data.

[0003] Although the pseudonym technology plays an important role in protecting the privacy of the vehicle, with an increase in the number of electric vehicles, a traditional centralized pseudonym distribution mechanism faces huge management overhead. In addition, a change timing of the vehicle pseudonym is also a key problem. The vehicle needs a universal metric standard to determine when to change the pseudonym to maximize privacy protection. Finally, an existing single-chain block chain system faces performance and latency problems in processing a large number of pseudonym transactions. SUMMARY

[0004] The application provides a V2G electric vehicle privacy protection method, system, device and medium, which are used to solve the performance and latency problems of an existing single-chain block chain system in processing a large number of pseudonym transactions.

[0005] Therefore, the first aspect of the application provides a V2G electric vehicle privacy protection method applied to a V2G electric vehicle privacy protection framework enabled by a cross-chain block chain pseudonym, the V2G electric vehicle privacy protection framework comprising: a charging management center composed of a main chain, a global trusted agency and a database, a plurality of charging stations composed of a pseudonym server, a regional trusted agency, a sub-chain and a charging pile, and a relay chain.

[0006] The method comprises the following steps:

[0007] S1, the regional trusted agency generates a pseudonym through multi-agent reinforcement learning technology and inventory theory, and stores the pseudonym in a pseudonym pool of the pseudonym server;

[0008] S2. When the pseudonym server receives a charging request from vehicle i, it determines whether vehicle i is using a pseudonym for privacy protection for the first time. If so, proceed to step S3; otherwise, proceed to step S4.

[0009] S3. The pseudonym server receives the initial registration request sent by vehicle i and generates a set of pseudonyms after verifying the ID and assigns them to vehicle i. Then, the global trusted organization generates a tracking list based on the pseudonyms and assigns it to each charging station. Step S5 is then executed.

[0010] S4. Vehicle i determines whether the pseudonyms in the local pseudonym server have been used up. If so, proceed to step S5; otherwise, proceed to step S8.

[0011] S5. Vehicle i requests a new pseudonym from the nearest pseudonym server. After verifying the ID of vehicle i in the subchain, the nearest pseudonym server generates a pseudonym registration block.

[0012] S6. The pseudonym server determines whether the request of vehicle i is a pseudonym request across charging stations. If not, proceed to step S7. If it is, the subchain and the subchain corresponding to the nearest pseudonym server perform pseudonym cross-chain verification through the relay chain. After successful verification, the pseudonym allocation right is transferred, and step S7 is executed.

[0013] S7. The subchain submits a cross-chain registration request to the relay chain, and after verification by the regional trusted institution and the relay chain, the cross-chain registration request is sent to the main chain.

[0014] S8. The pseudonym server assesses the current privacy level of the pseudonyms used to assign to trolleys based on the privacy entropy level, and determines whether to change the pseudonyms based on the assessment.

[0015] Optionally, step S3 specifically includes:

[0016] After receiving the initial registration request sent by vehicle i and verifying its ID, the pseudonym server obtains the public and private key pair and certificate of vehicle i from the regional trusted authority. When it receives a notification from the global trusted authority, it generates a set of pseudonyms based on the public and private key pair and certificate and assigns them to vehicle i.

[0017] The globally trusted authority creates a tracking table on the main chain to record the vehicle i's real identity, pseudonym identity, and pseudonym issuer. At the same time, the pseudonym server adds a pseudonym registration block on the sub-chain. Finally, the globally trusted authority uses the pseudonym issuer's public key to encrypt the tracking list and distributes it to each charging station, executing step S5.

[0018] Optionally, the regional trusted mechanism generates pseudonyms using multi-agent reinforcement learning techniques and inventory theory, specifically including:

[0019] The social welfare function of the charging station is determined based on the total utility of the vehicle and the utility of the regional credible institution. The social welfare function is maximized to determine several constraints.

[0020] The state space representation, action space representation, and reward function representation of the regional trust machine are established. The speaker-judge framework is used at the algorithm layer, and the multi-agent proximal policy optimization algorithm MAPPO is used for optimization, thereby determining the objective of the multi-agent proximal policy optimization algorithm MAPPO.

[0021] Based on the aforementioned constraints, pseudonyms are generated using the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm.

[0022] Optionally, the maximization of the social welfare function to determine several constraints is expressed as follows:

[0023]

[0024] in,

[0025]

[0026] 0 < c < p0 (9.3);

[0027] 0 < δ < βH (9.4);

[0028] In the formula, Let be the social welfare function. This represents the total utility of the vehicle. For the utility of the regional trusted institution, constraint (9.1) represents the upper limit of the number of charging station pseudonyms generated, constraint (9.2) represents the upper limit of the total number of charging station pseudonyms generated, θ represents the upper limit rate of the global trusted institution's registered pseudonym certificates, constraint (9.3) represents that the utility of the charging station pseudonym institution must be greater than 0, and constraint (9.4) represents that the utility of vehicle users must be greater than 0.

[0029] Optionally, the objective of the multi-agent proximal policy optimization algorithm MAPPO is represented as:

[0030]

[0031] In the formula, π θ A collective strategy for trusted regional institutions. For the strategy of regional trusted institution j, θ j θ and θ represent the strategies of the regional trusted institutions, respectively. and collective strategy π θ Hyperparameters.

[0032] Optionally, the pseudonym server assesses the current privacy level of the pseudonyms used to assign to trams based on the privacy entropy level, specifically including:

[0033] The pseudonym server calculates the privacy entropy level at time t using the privacy entropy function at time t for the pseudonyms used to assign to the trolley, thereby assessing the privacy level at time t.

[0034] Optionally, the privacy entropy function at time t is expressed as:

[0035]

[0036] In the formula, t and t i-1 These represent two different moments, p. i This represents the probability that an attacker can successfully track vehicle i after changing the pseudonym.

[0037] A second aspect of this application provides a V2G electric vehicle privacy protection system, comprising:

[0038] The first generation module is used by regional trusted institutions to generate pseudonyms using multi-agent reinforcement learning technology and inventory theory, and store them in the pseudonym pool of the pseudonym server.

[0039] The first judgment module is used to determine whether vehicle i is using a pseudonym for privacy protection for the first time when the pseudonym server receives a charging request sent by vehicle i. If so, the second generation module is triggered; otherwise, the second judgment module is triggered.

[0040] The second generation module is used for the pseudonym server to receive the initial registration request sent by vehicle i, perform ID verification, generate a set of pseudonyms and assign them to vehicle i. Then, the global trusted organization generates a tracking list based on the pseudonyms and assigns it to each charging station, triggering the third generation module.

[0041] The second judgment module is used by vehicle i to determine whether the pseudonyms in the local pseudonym server have been used up. If so, the third generation module is triggered; otherwise, the evaluation module is triggered.

[0042] The third generation module is used for vehicle i to request a new pseudonym from the nearest pseudonym server, and after the ID of vehicle i is verified in the subchain, the nearest pseudonym server generates a pseudonym registration block;

[0043] The third judgment module and the pseudonym server determine whether the request of vehicle i is a pseudonym request across charging stations. If not, the verification module is triggered. If it is, the subchain and the subchain corresponding to the nearest pseudonym server perform cross-chain pseudonym verification through the relay chain. After successful verification, the pseudonym allocation right is transferred and the verification module is triggered.

[0044] The verification module is used for subchains to submit cross-chain registration requests to the relay chain, and after verification by the regional trusted institution and the relay chain, the cross-chain registration request is sent to the main chain.

[0045] The evaluation module is used by the pseudonym server to assess the current privacy level of the pseudonyms used to assign to trolleys based on the privacy entropy level, and to determine whether to change the pseudonyms based on the assessment.

[0046] A third aspect of this application provides a V2G electric vehicle privacy protection device, the device comprising a processor and a memory:

[0047] The memory is used to store program code and transmit the program code to the processor;

[0048] The processor is configured to execute the steps of the V2G electric vehicle privacy protection method as described in the first aspect above, according to the instructions in the program code.

[0049] A fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the V2G electric vehicle privacy protection method described in the first aspect above.

[0050] As can be seen from the above technical solutions, this application has the following advantages:

[0051] This application provides a privacy protection method for V2G electric vehicles. Considering the potential privacy leaks in V2G charging scenarios, it proposes using pseudonym technology to replace real identities to prevent tracking. Then, this application integrates cross-chain technology into its V2G electric vehicle privacy protection framework; its decentralized architecture facilitates secure pseudonym distribution and revocation. Furthermore, this application proposes an analytical metric called privacy entropy to evaluate the degree of privacy protection after vehicle pseudonym changes. Combining privacy entropy with inventory theory, this application addresses the optimization problem of pseudonym generation in V2G pseudonym protection and employs a reinforcement learning-based algorithm to achieve efficient and economical pseudonym generation. This solves the performance and latency problems faced by existing single-chain blockchain systems when processing large numbers of pseudonym transactions. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a V2G electric vehicle privacy protection method provided in an embodiment of this application;

[0053] Figure 2 A V2G electric vehicle privacy protection framework empowered by cross-chain blockchain pseudonyms provided in the embodiments of this application;

[0054] Figure 3 This is a curve showing the change of pseudonym privacy entropy over time in the embodiments of this application;

[0055] Figure 4 This application provides a comparison of consensus times between single-chain and cross-chain systems in the embodiments of this application.

[0056] Figure 5 This refers to the consensus time corresponding to different transaction quantities under different system types provided in the embodiments of this application;

[0057] Figure 6 This is a comparison between the MAPPO average reward curve provided in the embodiments of this application and the kana generation task benchmark;

[0058] Figure 7 This is a schematic diagram of the structure of a V2G electric vehicle privacy protection system provided in the embodiments of this application. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0060] Please see Figure 1 This application provides a V2G electric vehicle privacy protection method, which is applied to a V2G electric vehicle privacy protection framework enabled by cross-chain blockchain pseudonyms. The V2G electric vehicle privacy protection framework includes: a charging management center consisting of a main chain, a global trusted institution and a database, several charging stations consisting of pseudonym servers, regional trusted institutions, sub-chains and charging piles, and a relay chain.

[0061] It should be noted that, Figure 2 The document showcases a V2G electric vehicle privacy protection framework enabled by cross-chain blockchain pseudonyms. The V2G charging and discharging management center is configured with a globally trusted authority (TA) and a pseudonym database in the cloud.

[0062] V2G electric vehicles update pseudonyms simultaneously during V2G charging and discharging at edge charging stations. The charging stations at the edge layer are configured with Local Authorities (LAs), which are qualified to perform pseudonym management tasks traditionally handled by global authorities. For example, LAs can quickly generate pseudonyms and distribute them to V2G vehicles at the charging station, significantly reducing the burden of publishing, storing, transmitting, and recording pseudonyms in the cloud center.

[0063] Furthermore, using blockchain technology to record pseudonyms can ensure their confidentiality. For example... Figure 2 As shown, the layered decentralized cross-chain architecture consists of a main chain, a relay chain, and multiple sub-chains. The main chain is maintained by a globally trusted institution with fully trusted nodes in the cloud, responsible for recording and verifying global pseudonyms and reports from trusted institutions in various regions. The relay chain facilitates the transmission of encrypted data, i.e., cross-chain requests between the main chain and sub-chains. Each charging station maintains a sub-chain at the edge layer, where edge pseudonym servers use distributed consensus to add pseudonym blocks, and the globally trusted institution acts as a notary to verify the cross-chain transactions of these blocks.

[0064] The methods include:

[0065] Step 101: The regional trusted agency generates pseudonyms using multi-agent reinforcement learning technology and inventory theory, and stores them in the pseudonym pool of the pseudonym server.

[0066] In one embodiment, the regional trusted agency in step 101 generates pseudonyms using multi-agent reinforcement learning techniques and inventory theory, specifically including:

[0067] The social welfare function of the charging station is determined based on the total utility of the vehicle and the utility of the regional credible institution. The social welfare function is maximized to determine several constraints.

[0068] The state space representation, action space representation, and reward function representation of the regional trust machine are established. The speaker-judge framework is used at the algorithm layer, and the multi-agent proximal policy optimization algorithm MAPPO is used for optimization, thereby determining the objective of the multi-agent proximal policy optimization algorithm MAPPO.

[0069] Based on the aforementioned constraints, pseudonyms are generated using the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm.

[0070] Step 102: When the pseudonym server receives a charging request from vehicle i, it determines whether vehicle i is using a pseudonym for privacy protection for the first time. If so, proceed to step 103; otherwise, proceed to step 104.

[0071] Step 103: The pseudonym server receives the initial registration request sent by vehicle i, verifies the ID, generates a set of pseudonyms, and assigns them to vehicle i. Then, the global trusted organization generates a tracking list based on the pseudonyms and assigns it to each charging station. Step 105 is then executed.

[0072] In one embodiment, step 103 specifically includes:

[0073] After receiving the initial registration request sent by vehicle i and verifying its ID, the pseudonym server obtains the public and private key pair and certificate of vehicle i from the regional trusted authority. When it receives a notification from the global trusted authority, it generates a set of pseudonyms based on the public and private key pair and certificate and assigns them to vehicle i.

[0074] The globally trusted authority creates a tracking table on the main chain to record the vehicle i's real identity, pseudonym identity, and pseudonym issuer. At the same time, the pseudonym server adds a pseudonym registration block on the sub-chain. Finally, the globally trusted authority uses the pseudonym issuer's public key to encrypt the tracking list and distributes it to each charging station, executing step 105.

[0075] The following should be noted regarding step 103:

[0076] A lightweight Boneh-Boyen short signature scheme is used for initial startup and key generation. When the vehicle uses its real identity ID... i When a device first joins the j-th charging station, it sends an initial registration request to the station's pseudonym server. The pseudonym server then verifies the ID. i Then, obtain the public / private key pair and corresponding certificate (PK) from the regional trusted authority. i SK i Cert i ).

[0077] Global Trusted Authority (TA) notifies Regional Trusted Authority (LA) j Assign a set of pseudonyms to vehicle i These pseudonyms are accompanied by corresponding public / private key pairs and certificates, represented as follows: Subsequently, the globally trusted organization creates a tracking table on the main chain MC to record the real and pseudonymous identities of vehicle i, as well as the pseudonym issuer (corresponding to LA). j Meanwhile, the pseudonym server is in the subchain SC. j Add a pseudonym registration block. Finally, the globally trusted authority uses LA. j After encrypting with the public key, the tracking list will be... Distribute to all charging stations, proceed to step 105.

[0078] Step 104: Vehicle i determines whether the pseudonyms in the local pseudonym server have been used up. If so, proceed to step 105; otherwise, proceed to step 108.

[0079] Step 105: Vehicle i requests a new pseudonym from the nearest pseudonym server. After verifying the ID of vehicle i in the subchain, the nearest pseudonym server generates a pseudonym registration block.

[0080] It should be noted that vehicle i directs to the nearest pseudonym server ES. mThe request includes the number of new pseudonyms requested, the current location, the public key, the pseudonym used, and the corresponding certificate, all using LA. j Public key encryption. In the subchain SC j After verifying the vehicle's identity, ES m Generate a pseudonym registration block.

[0081] Step 106: The pseudonym server determines whether the request from vehicle i is a pseudonym request across charging stations. If not, proceed to step 107. If it is, the subchain and the subchain corresponding to the nearest pseudonym server perform pseudonym cross-chain verification through the relay chain. After successful verification, the pseudonym allocation right is transferred, and step 107 is executed.

[0082] It should be noted that, in determining whether a request is a cross-charging station request, if the vehicle is currently moving between charging stations, then it is considered a cross-charging station request, and the subchain SC... j and SC m Cross-chain verification of pseudonyms is performed via a relay chain. If verification is successful, pseudonym allocation rights are transferred, and step 107 is executed. Otherwise, step 107 is executed directly.

[0083] Step 107, Subchain (SC) j The system submits a cross-chain registration request to the relay chain, and after verification by the regional trusted institution and the relay chain, the cross-chain registration request is sent to the main chain.

[0084] Step 108: The pseudonym server assesses the current privacy level of the pseudonyms used to assign to the trolleys based on the privacy entropy level, and determines whether to change the pseudonyms based on the assessment.

[0085] It should be noted that the pseudonyms assigned to electric vehicles are periodically assessed based on the privacy entropy level to determine whether to change the pseudonyms, thus protecting the privacy level of the V2G charging process.

[0086] Furthermore, electric vehicles can monitor and report malicious behavior by other V2G vehicles. Once a report is verified, the pseudonym can be removed and the vehicle can be added to a blacklist to ensure legitimate communication.

[0087] 1. The following explains the privacy entropy metric in step 108:

[0088] Based on the traditional definition of Age of Information (AoI), this application proposes an index called Privacy Entropy to quantify the privacy level after trolley pseudonym changes, letting p i Let represent the probability that an attacker successfully tracks vehicle i after changing their pseudonym, and let a and b represent the reciprocals of the maximum and minimum number of vehicles in the social hotspot, respectively. Then, time... After the pseudonym is changed, the privacy entropy can be represented as

[0089] H n =-log2p i ,p i ∈[a,b] (1)

[0090] Without loss of generality, this application considers the pseudonym-changing process to have low autocorrelation because the vehicle does not want an attacker to discover its pseudonym-changing pattern. Therefore, this application uses Figure 3 The exponential privacy entropy in the model addresses this situation, where the privacy entropy H(t) decreases exponentially over time, while the vehicle... When the current pseudonym is replaced with a new pseudonym, H(t) increases instantaneously. Therefore, the privacy entropy at time t can be defined as:

[0091]

[0092] This application uses the average privacy entropy over a time interval (0, J) to study the global impact of pseudonym changes, defined as... For the sake of simplicity, this application uses Figure 3 The privacy entropy is represented by the sum of irregular geometric regions in the data as follows:

[0093]

[0094] The process of changing the vehicle's pseudonym is modeled as a Poisson distribution with a rate of λ, and the probability of successful tracking follows a uniform distribution p. i ~U(a,b) can then be expressed as:

[0095]

[0096] 2. The following explains the utility functions for vehicle users and regional trusted institutions in step 101:

[0097] To quantify the benefits of privacy protection for vehicle users, this application states that the utility generated by user i consuming all requested pseudonyms during time t at charging station j is as follows:

[0098]

[0099] Its ε, β, δ neutralization This represents the basic cost of requesting a new pseudonym, the benefit of improving privacy protection by changing each pseudonym under unit privacy entropy, the additional cost of updating the routing table by changing each pseudonym, and the number of pseudonyms that vehicle i actually obtains at the j-th charging station.

[0100] Therefore, for a set J = {1,…,i,…,I} with a total of I vehicles, the total vehicle utility can be expressed as follows:

[0101]

[0102] in This represents the actual number of pseudonyms obtained by the vehicle at the start of time t at the j-th charging station. The requirement to indicate the pseudonym of the vehicle. This represents the number of pseudonyms generated in the j-th charging station. 3. The pseudonym generation strategy in step 101 is described below:

[0103] Pseudonym generation consumes computational resources, and an excess of pseudonyms can lead to storage overhead. Furthermore, when pseudonym generation fails to meet user needs, it can reduce user privacy and create security risks. This application, therefore, jointly studies the utility of vehicle and regional trusted institutions (LAs). The newsboy model is an important component of stochastic inventory theory, and this application uses it to study the optimization problem of LA pseudonym generation.

[0104] For LA j If, within time t, Excess kana must be retained in the kana pool for a period of time, incurring storage costs. Conversely, if LA j Those who fail to meet the pseudonym requirements will face penalties, therefore LA j The utility is expressed as follows:

[0105]

[0106] Where g, p0, c, h, and r represent the generation of each pseudonym, the profit of providing pseudonyms to vehicles, the communication overhead of allocating a unit of pseudonym, the cost of storing pseudonyms, and the penalty for unassigned pseudonyms, respectively.

[0107] In summary, the social welfare of the j-th charging station during time period T can be expressed as:

[0108]

[0109] Due to computational limitations, each LA j The number of generated kana should not exceed the maximum. Furthermore, the total number of pseudonyms within a local charging station cannot exceed a threshold. Therefore, the optimization problem of pseudonym management can be transformed into a problem of maximizing overall social welfare:

[0110]

[0111] 0 < c < p0 (9.3)

[0112] 0 < δ < βH (9.4)

[0113] Where constraint (9.1) represents the upper limit of the number of charging station pseudonyms generated, constraint (9.2) represents the upper limit of the total number of charging station pseudonyms generated, and θ represents the upper limit rate of registered pseudonym certificates of the globally trusted authority (TA). Constraint (9.3) indicates that the utility of the charging station pseudonym authority must be greater than 0, and constraint (9.4) indicates that the utility of vehicle users must be greater than 0.

[0114] Multi-agent reinforcement learning pseudonym generation strategy:

[0115] This application models the pseudonym generation of regional trusted institutions at multiple charging stations as a partially observable Markov decision process (POMDP). Based on the characteristics of POMDP, this application employs a multi-agent reinforcement learning algorithm based on edge learning technology to solve this problem, as follows:

[0116] (1) State-space representation: For each LA j This application will use the observations of the current decision step t. Defined as the union of observations over L steps, it is expressed as follows:

[0117]

[0118] in and They represent LA respectively j Average communication overhead with users within charging stations, periodic alias surplus, and vehicle alias demand.

[0119] (2) Action space representation: Each LA j This represents the number of kana generated starting at time t.

[0120] (3) Reward function representation: based on the current observation state LA based on reinforcement learning j Choose an action to receive a reward, then Transition to The reward for each agent in time period t for generating pseudonyms can be defined as... In vehicle-to-everything (V2G) scenarios, maximizing social welfare is the common goal of reward functions (LAs). Therefore, the reward function is the sum of rewards for LAs over time period t, defined as:

[0121]

[0122] At the algorithmic level, a speaker-judge framework is used, and the multi-agent proximal policy optimization algorithm MAPPO is employed for optimization. Policy iteration is achieved through centralized training and distributed execution. LA j strategy and collective strategy πθ The hyperparameters are respectively denoted as θ j and θ. Therefore, the objective of MAPPO can be expressed as:

[0123]

[0124] The following is a description of the simulation experiments provided in this application:

[0125] To evaluate the proposed cross-chain-assisted pseudonym management, this application conducted a simulation experiment. The simulation was performed on an Ubuntu 22.04 system equipped with an Intel Core i7-12700 CPU @ 2.10GHz and 8GB of memory, using a blockchain on the FISCO BCOS platform and a cross-chain platform called WeCros. By default, the number of pseudonym-related transactions was set to 1000, and the data size of each transaction was set to 1KB. Additionally, this application uses five sub-chains by default in the cross-chain system.

[0126] Figure 4 The diagram shows the consensus time for adding new blocks (such as pseudonymous registration information) in single-chain and cross-chain systems. Observing the solid red line, this application found that the single-chain system requires the longest average consensus time to complete 1000 transactions. In contrast, the cross-chain system using PBFT consensus in this application significantly reduces consensus time. As the number of subchains increases from 3 to 7, the average consensus time of the cross-chain system in this application decreases from 1.839 seconds to 0.679 seconds, indicating that the consensus efficiency of the cross-chain system in this application is nearly 6 times higher than the original. Furthermore, the time for adding blocks also increases due to the increased number of nodes participating in consensus. Figure 5 The impact of transaction volume on consensus time is shown. Clearly, both single-chain and cross-chain systems experience incremental block latency as the number of transactions increases. Nevertheless, the consensus time of the cross-chain system in this application increases smoothly, while the consensus time of the single-chain system increases more rapidly. Compared to the single-chain solution, the proposed solution significantly reduces consensus time by 87.973% when processing 2500 transactions, demonstrating its ability to handle high-throughput scenarios with pseudonym management.

[0127] like Figure 6As shown, this application compares the convergence performance of the proposed MAPPO-based scheme with several benchmark methods, including i) Multi-agent Deep Deterministic Policy Gradient (MADDPG), ii) Multi-agent Double-Delay Deep Deterministic Policy Gradient (MATD3), iii) Genetic Algorithm, iv) Stochastic Algorithm, and v) Greedy Algorithm. MADDPG and MATD3 are learning-based algorithms, while the Genetic Algorithm is a classic heuristic algorithm. In the stochastic scheme, the LA randomly determines the number of pseudonyms to be generated, while in the greedy scheme, the LA determines the number based on the maximum utility achieved in previous time steps. This application shows that the proposed scheme converges to the maximum reward, outperforming MATD3, MADDPG, Genetic Algorithm, Greedy Algorithm, and Stochastic Algorithm by 8.7%, 8.8%, 37.1%, 53.3%, and 92.9%, respectively. Since the pseudonym demand is time-varying, traditional heuristic algorithms cannot achieve convergence. Therefore, the MAPPO-based solution in this application requires less training time and performs better, highlighting its capabilities in pseudonym generation.

[0128] This application provides a cross-chain blockchain-enabled pseudonym-based privacy protection method for V2G electric vehicles. Considering the potential privacy leaks in V2G charging scenarios, it proposes using pseudonym technology to replace real identities to prevent tracking. Then, this application integrates cross-chain technology into its framework, whose decentralized architecture facilitates secure pseudonym distribution and revocation. Furthermore, this application proposes an analytical metric called privacy entropy to evaluate the degree of privacy protection after vehicle pseudonym changes. Combining privacy entropy with inventory theory, this application proposes an optimization problem for pseudonym generation in V2G pseudonym protection and employs a reinforcement learning-based algorithm to achieve efficient and economical pseudonym generation. Finally, numerical results demonstrate the effectiveness and feasibility of the proposed framework in V2G scenarios.

[0129] The above is a V2G electric vehicle privacy protection method provided in the embodiments of this application. The following is a V2G electric vehicle privacy protection system provided in the embodiments of this application.

[0130] Please see Figure 7 The V2G electric vehicle privacy protection system provided in this application embodiment includes:

[0131] The first generation module 201 is used by the regional trusted agency to generate pseudonyms using multi-agent reinforcement learning technology and inventory theory, and store them in the pseudonym pool of the pseudonym server.

[0132] The first judgment module 202 is used to determine whether vehicle i is using a pseudonym for privacy protection for the first time when the pseudonym server receives a charging request sent by vehicle i. If so, the second generation module 203 is triggered; otherwise, the second judgment module 204 is triggered.

[0133] The second generation module 203 is used to generate a set of pseudonyms after the pseudonym server receives the initial registration request sent by vehicle i and performs ID verification, and then assigns them to vehicle i. After that, the global trusted organization generates a tracking list based on the pseudonyms and assigns it to each charging station, triggering the third generation module 205.

[0134] The second judgment module 204 is used by vehicle i to determine whether the pseudonyms in the local pseudonym server have been used up. If so, the third generation module 205 is triggered; otherwise, the evaluation module 208 is triggered.

[0135] The third generation module 205 is used for vehicle i to request a new pseudonym from the nearest pseudonym server, and after the ID of vehicle i is verified in the subchain, the nearest pseudonym server generates a pseudonym registration block.

[0136] The third judgment module 206, the pseudonym server judges whether the request of vehicle i is a pseudonym request across charging stations. If not, the verification module 207 is triggered. If it is, the subchain and the subchain corresponding to the nearest pseudonym server perform pseudonym cross-chain verification through the relay chain. After successful verification, the pseudonym allocation right is transferred and the verification module is triggered.

[0137] Verification module 207 is used for subchains to submit cross-chain registration requests to the relay chain, and after verification by the regional trusted institution and the relay chain, the cross-chain registration request is sent to the main chain.

[0138] The evaluation module 208 is used by the pseudonym server to evaluate the current privacy level of the pseudonyms used to assign to the trolleys based on the privacy entropy level, and to determine whether to change the pseudonyms based on the evaluation results.

[0139] Furthermore, this application embodiment also provides a V2G electric vehicle privacy protection device, the device including a processor and a memory:

[0140] The memory is used to store program code and transmit the program code to the processor;

[0141] The processor is used to execute the steps of the V2G electric vehicle privacy protection method as described in the above method embodiments, according to the instructions in the program code.

[0142] Furthermore, this application embodiment also provides a computer-readable storage medium for storing program code for executing the methods described in the above method embodiments.

[0143] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0144] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0145] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0150] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A V2G electric vehicle privacy protection method, characterized in that, The V2G electric vehicle privacy protection framework applied to cross-chain blockchain pseudonym empowerment comprises a charging management center composed of a main chain, a global trusted agency and a database, a plurality of charging stations composed of a pseudonym server, a regional trusted agency, a sub-chain and a charging pile, and a relay chain; The method comprises: S1, the regional trusted agency generates pseudonyms through multi-agent reinforcement learning technology and inventory theory, and stores them in the pseudonym pool of the pseudonym server; S2, when the pseudonym server receives the charging request sent by vehicle i, it is judged whether vehicle i is the first time to use pseudonyms for privacy protection, if yes, step S3 is executed, otherwise step S4 is executed; S3, the pseudonym server receives the initial registration request sent by vehicle i and performs ID verification, then generates a set of pseudonyms and assigns them to vehicle i, then the global trusted agency generates a tracking list according to the pseudonyms and assigns it to each charging station, and executes step S5; S4, vehicle i judges whether the pseudonyms in the local pseudonym server are used up, if yes, step S5 is executed, otherwise step S8 is executed; S5, vehicle i requests new pseudonyms from the nearest pseudonym server, and after ID verification of vehicle i on the sub-chain, the nearest pseudonym server generates a pseudonym registration block; S6, the pseudonym server judges whether the request of vehicle i is a cross-charging station pseudonym request, if not, step S7 is executed, if yes, the sub-chain and the corresponding sub-chain of the nearest pseudonym server perform cross-chain verification through the relay chain, and after the verification is successful, the pseudonym allocation right is transferred, and step S7 is executed; S7, the sub-chain submits a cross-chain registration request to the relay chain, and after verification by the regional trusted agency and the relay chain, the cross-chain registration request is sent to the main chain; S8, the pseudonym server evaluates the current privacy level according to the privacy entropy level for the pseudonyms assigned to the electric vehicle, and determines whether to change the pseudonyms according to the evaluation.

2. The V2G electric vehicle privacy protection method of claim 1, wherein, Step S3, specifically comprising: After the pseudonym server receives the initial registration request sent by vehicle i and performs ID verification, it obtains the public-private key pair and certificate of vehicle i from the regional trusted agency, and generates a set of pseudonyms according to the public-private key pair and the certificate and assigns them to vehicle i when receiving the notification of the global trusted agency; The global trusted agency creates a tracking table on the main chain to record the real identity and pseudonym identity of vehicle i and the pseudonym issuer, and at the same time, the pseudonym server adds a pseudonym registration block on the sub-chain, finally, the global trusted agency encrypts the tracking list using the public key of the pseudonym issuer and distributes it to each charging station, and executes step S5. 3.The V2G electric vehicle privacy protection method of claim 1, wherein, The regional trusted agency generates pseudonyms through multi-agent reinforcement learning technology and inventory theory, specifically comprising: Determine the social welfare function of the charging station according to the total utility of the vehicle and the utility of the regional trusted agency, maximize the social welfare function, and thus determine a plurality of constraint conditions; Establish the state space representation, action space representation and reward function representation of the regional trusted agency, use the preacher-judge framework at the algorithm level, and use the multi-agent proximal policy optimization algorithm MAPPO for optimization, so as to determine the target of the multi-agent proximal policy optimization algorithm MAPPO. Based on the constraint conditions, a pseudonym is generated by a multi-agent proximal policy optimization algorithm MAPPO.

4. The V2G electric vehicle privacy protection method of claim 3, wherein, The social welfare function is maximized to determine the constraint conditions, which are expressed as: ； wherein ; （9.1）； （9.2）； （9.3）； （9.4）； wherein, is the social welfare function, is the total utility of the vehicles, is the utility of the regional trusted authority, constraint (9.1) represents the upper limit of the number of generated pseudonyms for charging stations, and constraint (9.2) represents the upper limit of the total number of all charging station pseudonyms, represents the upper limit rate of the registration pseudonym certificate of the global trusted authority, constraint (9.3) represents that the utility of the charging station pseudonym authority is greater than 0, constraint (9.4) represents that the utility of the vehicle user is greater than 0, t is the time, T is the time period, j is a certain charging pile, J is the total number of charging piles, c is the communication overhead of the allocation unit pseudonym, p0 is the profit of providing pseudonyms to vehicles, is the number of pseudonyms generated in the jth charging station, is the additional cost of updating the routing table for each pseudonym, is the benefit of improving the privacy protection level by changing each pseudonym under the unit privacy entropy.

5. The V2G electric vehicle privacy protection method of claim 3, wherein, The goal of the multi-agent proximal policy optimization algorithm MAPPO is expressed as: ； wherein is a collective policy of regional trusted authorities, is a policy of regional trusted authority y, Y being the total number of regional trusted authorities, and are hyperparameters of the policy and the collective policy of regional trusted authorities, respectively.

6. The V2G electric vehicle privacy protection method of claim 1, wherein, The pseudonym server evaluates the current privacy level according to the privacy entropy level for the pseudonym assigned to the electric vehicle, specifically including: The pseudonym server calculates a privacy entropy function for the time point, based on the pseudonym assigned to the vehicle the time point, thereby evaluating the privacy entropy level for the time point, thereby evaluating the privacy level for the time point.

7. The V2G electric vehicle privacy protection method of claim 6, wherein, The The privacy entropy function at time instant is represented as: ； wherein, and denote two different time instants, denotes the probability that the attacker succeeds in tracking vehicle i after the pseudonym change.

8. A V2G electric vehicle privacy protection system, characterized in that, Including: A first generation module for regional trusted agencies to generate pseudonyms through multi-agent reinforcement learning technology and inventory theory, and store them in the pseudonym pool of the pseudonym server; A first judgment module for the pseudonym server to receive a charging request sent by vehicle i, and determine whether vehicle i is using a pseudonym for the first time for privacy protection, if so, trigger the second generation module, otherwise trigger the second judgment module; A second generation module for the pseudonym server to receive an initial registration request sent by vehicle i and perform ID verification, generate a set of pseudonyms and assign them to vehicle i, and then trigger the third generation module after the global trusted agency generates a tracking list according to the pseudonyms and assigns it to each charging station; A second judgment module for vehicle i to determine whether the pseudonyms in the local pseudonym server have been used up, if so, trigger the third generation module, otherwise trigger the evaluation module; A third generation module for vehicle i to request new pseudonyms from the nearest pseudonym server, and after the ID verification of vehicle i in the sub-chain, the nearest pseudonym server generates a pseudonym registration block; A third judgment module, the pseudonym server determines whether the request of vehicle i is a cross-charging station pseudonym request, if not, trigger the verification module, if so, the sub-chain and the corresponding sub-chain of the nearest pseudonym server perform cross-chain verification of the pseudonym through the relay chain, after the verification is successful, the pseudonym allocation right is transferred, and then trigger the verification module; A verification module for the sub-chain to submit a cross-chain registration request to the relay chain, and after verification by the regional trusted agency and the relay chain, the cross-chain registration request is sent to the main chain; An evaluation module for the pseudonym server to evaluate the current privacy level according to the privacy entropy level for the pseudonym assigned to the electric vehicle, and determine whether to change the pseudonym according to the evaluation.

9. A V2G electric vehicle privacy protection device, characterized by, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the V2G electric vehicle privacy protection method according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium is used to store program code for executing the V2G electric vehicle privacy protection method according to any one of claims 1-7.

Images