Electric bicycle data collaborative sharing method based on multi-party safety calculation

Abstract

According to the electric bicycle data collaborative sharing method based on multi-party security computing, the collaborative computing system is constructed, the collaborative control nodes in the collaborative computing system are independent of the business departments, and the collaborative computing system allocates at least one computing execution node to each business department. The calculation execution nodes are in communication connection with a database of a business department, and each calculation execution node is in communication connection with the coordination control node; the method comprises the following steps: splitting a calculation task in a collaborative governance service related to cross-department data sharing based on service logic, and obtaining a single-department calculation sub-task and a cross-department calculation sub-task after splitting; each calculation execution node executes a calculation task which only relates to the data of the business department, and only submits a calculation result to the coordination control node; all calculation tasks related to cross-department data are completed at the coordination control node; and then the coordination control node feeds back the data requested by the business department to the requesting department.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an electric bicycle data collaborative sharing method based on multi-party secure computation. BACKGROUND

[0002] In recent years, with the continuous growth of the number of electric bicycles, social problems related to electric bicycles have also increased year by year. In April 2024, in order to carry out the rectification action of electric bicycle safety hazards, it is required that the relevant departments of traffic management, public security, market supervision, emergency management, etc. strengthen the coordination and supervision in the aspects of electric bicycle production, sales, use, parking, charging, management, etc. Efficient and seamless cross-department and cross-industry collaborative governance needs to be supported by cross-department data sharing services. For example, by analyzing the passing track, violation, accident, etc. of electric bicycles, the traffic management department can optimize the urban traffic planning, alleviate the congestion, and reduce the traffic violation and traffic accident rate; the market supervision department can obtain the clues of illegal modification through data analysis, and manage the manufacturers and sales stores of electric bicycles at the source; the express delivery and take-out enterprises can supervise the riders by analyzing the violation record data of the riders, and impose fines and bans on the riders; the manufacturers can optimize product design according to the riding habit data of users. However, in actual work, the electric bicycle related data involves a large amount of personal privacy information, such as the identity of the owner, the riding track, the use habit, the traffic violation record, etc. Directly sharing the original data across departments will face the risk of privacy data leakage. SUMMARY

[0003] In order to solve the problem of great risk of privacy data leakage in the cross-department electric bicycle data sharing in the prior art, the present application provides an electric bicycle data collaborative sharing method based on multi-party secure computation, which can realize the sharing of electric bicycle data between different participants under the premise of protecting data privacy.

[0004] The technical solution of the present application is as follows: an electric bicycle data collaborative sharing method based on multi-party secure computation, characterized in that it comprises the following steps: S1: analyzing each collaborative governance business involving cross-department data sharing, obtaining business logic and calculation tasks included in the business logic; The business logic describes all business data used in each collaborative governance business and the relationship between the business data, and the relationship between the business data constitutes a calculation task; S2: obtaining calculation sub-tasks by splitting the calculation tasks; Naming and generating a unique sub-task ID for each calculation sub-task; The computing sub-tasks include single-department computing sub-tasks and cross-department computing sub-tasks; each of the computing sub-tasks includes N computing task models, where N≥1; The single-department computing sub-tasks are computing tasks using data provided by only one business department in the computing process; each single-department computing sub-task type of computing sub-task sets a unique associated business department; The cross-department computing sub-tasks are computing tasks using data provided by more than one business department in the computing process; the cross-department computing sub-tasks do not set corresponding business departments; S3: Construct a data collaborative computing system; The collaborative computing system includes a coordination control node and a plurality of computing execution nodes; the number of the computing execution nodes is greater than or equal to 1; each of the computing execution nodes is in communication connection with the coordination control node; The coordination control node exists independently of the business departments; The business logic corresponding to each of the collaborative governance businesses and all the computing sub-tasks are saved in the coordination control node; Each business department is allocated at least one computing execution node, which is in communication connection with a data providing node of the business department; S4: Each business department submits a computing request of the current collaborative governance business to the coordination control node; The computing request includes the name of the collaborative governance business, request data, a data request range, and a use purpose; The request data is business data that the requester hopes to obtain through the computing of the current collaborative governance business; the request data includes all or part of the output data of each department in the collaborative governance business; The data request range is used to specify the time range, geographical range, and other limiting conditions for collecting original data uniformly by all business departments; The use purpose is used to describe the request reason of the request data; S5: After receiving the collaborative computing request, the following operations are performed in the collaborative computing system: a1: The coordination control node confirms the associated business departments involved in each of the computing requests; a2: The coordination control node allocates corresponding computing tasks to each computing execution node corresponding to the associated business departments according to the single-department computing sub-tasks involved in the computing request and delivers the data request range of the current computing task; The cross-department computing sub-tasks are reserved in the coordination control node; a3: The computing execution node executes the received single-department computing sub-task within the scope of the data request according to the business logic corresponding to the collaborative computing request, and only submits the computing result to the coordination control node after the computing is completed; a4: Based on the business logic corresponding to the collaborative computing request, receive the computing results of each computing execution node's returned computing sub-task; and simultaneously execute the cross-departmental computing sub-task in the coordination control node; a5: The collaborative computing system will transmit the requested data that needs to be fed back to the requesting party.

[0005] Its further features are: The collaborative computing system also includes: a result verification node; The result verification node is communicatively connected to the coordination and control node; The coordination and control node sends the request data that needs to be fed back to the result verification node; the result verification node verifies the request data that needs to be fed back based on business logic requirements and judges the rationality of the data; If the data meets the business logic requirements, it will be returned to the requester. If data does not meet the requirements of the business logic, further processing will be carried out according to the specific circumstances. Step S1 also includes the following operations: Build a computational task model based on collaborative governance business; The computational task model includes: computational model name, computational model ID, input parameters, computational logic, and output parameters; The input parameters include the data that the computation task needs to process, including: data type, data description, and data unit; The computational logic describes the core computational logic of the computational task, and defines the computational method for each input parameter, including: operation, transformation and / or logical judgment; The output parameter is the final result obtained after the input parameter is processed according to the calculation logic. The output parameter includes: data type, data description and data unit. The business units include: traffic management departments, market supervision departments, and vehicle-using companies; the vehicle-using companies include: express delivery and food delivery companies and car-sharing and rental companies. Step S1 also includes: b1: Define the raw data types that each business department is responsible for collecting related to business processing, and define a unique data identifier for each raw data type; The raw data collected by the traffic management department includes: static traffic management data and dynamic traffic management data; the static traffic management data includes: electric bicycle product name, model, manufacturer, production plant, vehicle identification number, motor number, vehicle weight, maximum design speed, motor power, and license plate registration information; the license plate registration information includes: owner information such as name, contact information, and address, license plate number, and license plate type; the dynamic traffic management data includes: electric bicycle travel routes, violations, and accidents; The raw data collected by the market supervision department includes: product name, model, manufacturer, production plant, vehicle identification number, and sales outlet information; The raw data collected by the vehicle-using companies includes: rider's name, contact information, vehicle identification number (VIN) and license plate number of the electric bicycle used; b2: Standardize data processing methods; The data standardization processing method includes: converting data according to a unified data format and standard; In the data collaborative computing system, each node performs the following operations after joining: c1: All nodes complete identity registration, generate unique identifiers, and register roles before deployment; c2: The coordination and control node generates the public parameters required for the multi-party secure computation protocol through a distributed key generation protocol; each node generates a local public-private key pair for encrypted communication and signing; In step a2, when the coordination control node sends the task to the computing execution node, the specific steps include: d1: The coordination and control node generates a separate instruction for each of the single-department computing subtasks; The instructions include: basic information, data parameters, calculation parameters, and security parameters; The basic information includes: subtask ID and subtask name; the calculation parameters include: the calculation task model included in the subtask; the data parameters include: the data identifier and data request range of the input parameters in the calculation task model; the security parameters include: encryption algorithm type and signature algorithm type. d2: The coordination and control node confirms the business department corresponding to the single-department calculation subtask, and records the calculation execution node of the business department as the target node; d3: The coordination and control node encrypts the instruction content according to the security parameters and then distributes it to the target node through a secure channel; Before step S5 is executed, the following steps also need to be performed in the collaborative computing system: e1: Select a multi-party secure computation protocol for the computational tasks executed in the system; The multi-party secure computation protocol includes: a secret-sharing-based protocol, a garbled circuit protocol, or an unintentional transmission protocol; e2: Select the corresponding homomorphic encryption algorithm for each computation request; The homomorphic encryption algorithms include: additive homomorphic Paillier, hierarchical homomorphic LHE, fully homomorphic FHE, and BFV homomorphic; The settings for the homomorphic encryption algorithm include: selecting only one, or selecting multiple based on classification encryption modes.

[0006] This application provides a method for collaborative sharing of electric bicycle data based on multi-party secure computation. It constructs a collaborative computing system where the coordination and control node exists independently of the business departments. The collaborative computing system allocates at least one computation execution node to each business department, and these nodes communicate with the business department's database and the coordination and control node. Based on business logic, computational tasks in collaborative governance processes involving cross-departmental data sharing are broken down into single-department and cross-departmental computational sub-tasks. Each computation execution node executes computational tasks involving only its own business department's data and submits only the results to the coordination and control node. All computational tasks involving cross-departmental data are completed at the coordination and control node. The coordination and control node then feeds back the data requested by the business department to the requesting department. Each business department can only obtain the requested business data and will not access intermediate business data, ensuring data privacy while enabling the sharing of electric bicycle data among different participants. Attached Figure Description

[0007] Figure 1 This is a flowchart illustrating a data collaborative sharing method for electric bicycles based on multi-party secure computation. Figure 2 A block diagram of a data collaborative computing system; Figure 3 This is an example of the output format for Example 3. Detailed Implementation

[0008] like Figure 1 As shown, this application includes a method for collaborative data sharing of electric bicycles based on multi-party secure computation, which includes the following steps.

[0009] S1: Analyze each existing collaborative governance business involving cross-departmental data sharing to obtain the business logic and the computational tasks included in the business logic; the business logic describes all the business data used in each collaborative governance business and the relationships between the business data, and the relationships between the business data constitute the computational tasks.

[0010] The computational model in this application is constructed based on collaborative governance business. The computational task model can be existing mathematical calculations such as addition, subtraction, multiplication, division, intersection, and union; it can be statistical analysis calculations; or it can be logical judgments or transformation operations customized according to actual business needs.

[0011] The computation task model includes: computation model name, computation model ID, input parameters, computation logic, and output parameters.

[0012] Computational task models are distinguished by a unique identifier called a computational model ID; Input parameters include the data that the computation task needs to process, including: data type, data description, and data unit; The computational logic describes the core computational logic of the computational task, defining the computational methods for each input parameter, including: operations, transformations, and / or logical judgments; For example: Calculation logic formula: Total demerit points = Σ (points deducted for each violation); Speeding threshold = 30km / h; The output parameters are the final results obtained after the input parameters are processed according to the calculation logic. The output parameters include: data type, data description, and data unit.

[0013] The business units include: traffic management departments, market supervision departments, and car-using companies; car-using companies include: express delivery and food delivery companies and car-sharing and rental companies.

[0014] b1: Define the raw data types that each business department is responsible for collecting related to business processing, and define a unique data identifier for each raw data type; The raw data collected by traffic management departments includes: static traffic management data and dynamic traffic management data; static traffic management data includes: electric bicycle product name, model, manufacturer, production plant, vehicle identification number, motor number, vehicle weight, maximum design speed, motor power, and license plate registration information; license plate registration information includes: owner information such as name, contact information, and address, license plate number, and license plate type; dynamic traffic management data includes: electric bicycle travel routes, violations, and accidents; The raw data collected by the market supervision department includes: product name, model, manufacturer, production plant, vehicle identification number, and sales outlet information; The raw data collected by the vehicle-using companies includes: rider's name, contact information, vehicle identification number (VIN) and license plate number of the electric bicycle used.

[0015] To ensure the standardization and consistency of data during the multi-party secure computation model calculation, all parties involved in the collaborative sharing of electric bicycle data need to clean and preprocess the collected data. Considering that the data collected from various industries and departments may contain noisy data, duplicate data, and outliers, affecting subsequent calculations and statistical analysis of the multi-party secure computation model, data cleaning is necessary. For example, text data can be cleaned by removing extra spaces and processing special characters; outliers can be verified through range checks, consistency checks, and business rule validation. Specific operations will be implemented based on existing technologies.

[0016] b2: Standardize data processing methods; Data standardization processing methods include: converting data according to a unified data format and standard.

[0017] Different industries and departments typically collect data related to electric bicycles according to their own standards, which may lead to differences in date formats, units, text case, telephone numbers, and other formats. Therefore, the data needs to be converted and standardized according to a unified data format and standard. For example, the speed unit recorded by different manufacturers should be standardized to km / h, and geographical location information should be standardized to latitude and longitude format.

[0018] S2: The computation task is split into computation subtasks.

[0019] Name each computational subtask and generate a unique subtask ID; The computational subtasks include: single-department computational subtasks and cross-departmental computational subtasks; each computational subtask includes N computational task models, where N≥1; A single-department calculation subtask is a calculation task that uses only data provided by a single business department during the calculation process; each single-department calculation subtask type is assigned a unique associated business department. Cross-departmental calculation subtasks are those where the data used in the calculation process is provided by more than one business department; cross-departmental calculation subtasks do not have a corresponding business department set.

[0020] In practice, all business departments can jointly negotiate and clarify the calculation sub-tasks that each department needs to perform. For example, traffic management departments, in conjunction with express delivery and food delivery companies and electric bicycle manufacturers, can calculate the riding credit score of express delivery and food delivery riders based on data such as license plate numbers, violations, accidents, riders, riders' companies, and order assignments. Market supervision departments, in conjunction with traffic management departments, electric bicycle sales companies or stores, and electric bicycle manufacturers, can analyze data such as section speed measurement, vehicle brand and model, and the store where the vehicle was purchased to identify clues about illegal modifications to electric bicycles. Electric bicycle manufacturers, in conjunction with traffic management departments and emergency management departments, can analyze the potential risks and hazards of electric bicycle products based on data such as vehicle registration, accidents, and batteries.

[0021] For example, in Implementation 1, the collaborative governance business is to "calculate the riding credit score of express delivery and food delivery riders". The overall business logic is "credit score = basic score + compliance reward score - violation deduction score - accident deduction score". The calculation tasks include: finding all traffic records of all food delivery riders; calculating the basic score, calculating the compliance reward score, calculating the violation deduction score, and calculating the accident deduction score.

[0022] The associated business data includes: electric bicycle license plate numbers, violation records, and accident records provided by traffic management departments; and electric bicycle license plate numbers, rider personal information, affiliated companies, and order details provided by express delivery and food delivery companies.

[0023] The computational task is broken down into computational subtasks: The single-department calculation subtasks in the subtasks include: The business logic of subtask 1 of a single department is as follows: The traffic management department calculates the violation records obtained by the execution node based on the license plate of the electric bicycle and calculates the violation points (such as 5 points for running a red light and 3 points for speeding).

[0024] The business logic of subtask 2 of a single department is as follows: The traffic management department calculates the execution node to obtain the accident record based on the electric bicycle license plate and calculates the accident deduction points (e.g., 10 points are deducted for a fully responsible accident and 5 points are deducted for a secondary responsible accident).

[0025] The business logic of sub-task 3 for a single department is as follows: The express delivery and food delivery companies calculate the rider's order completion status based on the rider's personal information and calculate compliance reward points (such as 10 points for no overdue orders in a month and 5 points for a customer satisfaction rate of over 95%).

[0026] The cross-departmental computing subtasks in the subtasks include: The business logic of the cross-departmental sub-task is as follows: based on the electric bicycle license plate information, traffic management departments and express delivery and food delivery companies securely calculate the rider's violation records, accident records, and order completion status. Among them, the traffic management department is responsible for the secure calculation of electric bicycle license plates, violation records, and accident records, while the express delivery and food delivery companies are responsible for the secure calculation of rider personal information, electric bicycle license plates, affiliated companies, and order completion information.

[0027] For example, in Example 2, the collaborative governance business is "analyzing clues about illegal modifications of electric bicycles", and the core business logic is "comparing the maximum design speed registered for the vehicle with the actual speed measured in the section, and filtering out vehicles that exceed the design speed". The associated business data includes: the maximum design speed for registered vehicles provided by the market supervision department, vehicle registration data and dynamic section speed measurement data provided by the traffic management department, and vehicle sales store information, manufacturer information, and other data provided by the sales stores.

[0028] The computational task is broken down into computational subtasks: The single-department calculation subtasks include: The business logic of subtask 1 of a single department is as follows: The traffic management department calculates the average actual driving speed based on the vehicle's section speed measurement data at the execution node.

[0029] Cross-departmental computing subtasks include: The business logic of cross-departmental sub-task 1 is as follows: The market supervision department and the traffic management department, based on the vehicle's registered maximum design speed, safely calculate the license plate information and vehicle identification number information of electric bicycles whose average actual driving speed exceeds the registered maximum design speed.

[0030] The business logic of cross-departmental sub-task 2 is as follows: the market supervision department and the sales store obtain the sales store information, manufacturer information, etc. corresponding to the vehicle code information by securely intersecting the vehicle code information.

[0031] S3: Build a collaborative data computing system; like Figure 2 As shown, the collaborative computing system includes: a coordination control node, a computation execution node, a result verification node, and a data provision node; the number of computation execution nodes is greater than or equal to 1; each computation execution node is communicatively connected to the coordination control node; and the result verification node is communicatively connected to the coordination control node.

[0032] The coordination and control node and the result verification node exist independently of the business departments. In practical applications, the coordination and control node and the result verification node can be set up in the common superior unit of all business departments, or they can be set up in a secure third-party institution, such as the big data department of a local government.

[0033] The business logic and all computational subtasks corresponding to each collaborative governance business are stored in the coordination and control node; at least one computational execution node is allocated to each business department, and the computational execution node communicates with the data providing node of the business department.

[0034] The coordination and control node sends the request data that needs to be fed back to the result verification node; the result verification node verifies the request data that needs to be fed back based on the business logic requirements; if it meets the business logic requirements, it feeds the data back to the requester; if there is data that does not meet the business logic requirements, it feeds back the verification failure to the requester, and the result verification node performs subsequent processing according to the specific situation.

[0035] The main verification items of the result verification node in this application do not involve the original data or the numerical accuracy of the results, but rather judge the reasonableness of the calculation results based on the business level. In specific implementation, for each type of collaborative governance business-related data, the business-level judgment logic is pre-stored as a constraint in the result verification node. After the coordination and control node sends the requested data that needs to be fed back to the result verification node, it judges the reasonableness of the data according to the constraint conditions corresponding to the data type. For example, for an electric bicycle license plate, if the calculation results show dozens of accident records for a certain day, this is clearly not within the reasonable range.

[0036] The data provider node provides private data to each computing execution node, specifically implemented using databases or similar methods.

[0037] In a collaborative data computing system, each node performs the following operations after joining: c1: All nodes complete identity registration before deployment, generate a unique identifier: node ID, and register roles. Role types include: coordination and control node, computation execution node, and result verification node; c2: The coordinating control node generates the public parameters required for the multi-party secure computation protocol via the Distributed Key Generation (DKG) protocol, such as a secretly shared prime modulus. Each node generates a local public-private key pair for encrypted communication and signing. The specific encryption and decryption methods are implemented based on existing technologies.

[0038] Before step S5 is executed, the following steps must also be performed in the collaborative computing system: e1: Select a multi-party secure computation protocol for the computational tasks executed in the system; Multi-party secure computation protocols include: secret-sharing protocols, obfuscated circuit protocols, or unintentional transmission protocols.

[0039] The multi-party secure computation protocol can be configured individually for each computation request, or it can be configured uniformly for all requests. In practice, you can use the same protocol for all computation types, or you can select different protocols for different types of computations.

[0040] For example, for simple calculation tasks such as summation and averaging, a secret-sharing-based protocol can be used; for tasks involving more complex logical operations, a garbled circuit protocol can be used.

[0041] e2: After selecting a multi-party security protocol, it is also necessary to select the corresponding homomorphic encryption algorithm for each computation request; Homomorphic encryption algorithms include: additive homomorphic Paillier, hierarchical homomorphic LHE, fully homomorphic FHE, and BFV homomorphic; The settings for homomorphic encryption algorithms include: selecting only one mode applicable to all computing tasks, or selecting multiple modes based on classification encryption modes, setting different homomorphic encryption algorithms for different types of computing tasks.

[0042] In actual deployment, the computation execution nodes of the participating parties can be computing resources provided by the participating parties themselves or dedicated third-party computing service providers. Considering the security of electric bicycle data during storage and transmission between different platforms, data encryption protection is necessary. Simultaneously, to enable the multi-party secure computation model to directly perform specific calculations on the ciphertext data without decryption, homomorphic encryption algorithms are used to encrypt the data. Homomorphic encryption algorithms typically include additive homomorphic (Paillier), hierarchical homomorphic (LHE), and fully homomorphic (FHE). To balance encryption efficiency and security, this method adopts a classified encryption mode. For statistical scenarios such as summation, mean, and median of multi-party data, where multiplication or low-order multiplication is unnecessary, Paillier or BFV (low-order multiplication) can be used, resulting in high computational efficiency and simple implementation. For scenarios such as machine learning training and inference that require simultaneous support for addition and multiplication operations, hierarchical homomorphic or fully homomorphic encryption is used, offering strong versatility. After each participating party's computation execution node completes its execution, it obtains its respective ciphertext result data.

[0043] S4: Each business unit submits a computation request for this collaborative governance task to the coordination control node. In this method, only business units registered in the system can initiate computation requests. The computation request is sent directly to the coordination control node. After receiving the data sharing request, the coordination control node verifies the identity of the requester and checks whether the request complies with the pre-set sharing policies and permissions.

[0044] The computation request includes: the name of the collaborative governance business, the requested data, the scope of the data request, and the purpose of its use; The requested data refers to the business data that the requesting party wishes to obtain through the calculations of this collaborative governance operation; the requested data includes all or part of the output data of each department in the collaborative governance operation; The data request scope specifies the time range, geographical scope, and other restrictions for all business departments to uniformly collect raw data. This is because in actual calculations, a time range is usually specified, such as the past year, with each business department collecting raw data within that specified time frame. Alternatively, a geographical range can be specified; the default is the entire city, but when specific districts within the city are specified, business departments will only collect raw data within those designated areas for subsequent calculations. Other restrictions can also be configured based on actual business requests.

[0045] Of course, the scope of data requests can also be limited through the computational task model. For example, when different business departments collect data in different ranges, the scope can be limited through the computational task model.

[0046] Purpose of use: Used to describe the reason for requesting data.

[0047] S5: Upon receiving a collaborative computing request, perform the following operations in the collaborative computing system.

[0048] a1: The coordination and control node confirms the relevant business departments involved in each computing request.

[0049] a2: The coordination and control node, based on the single-department computational sub-tasks involved in the computation request, assigns the corresponding computational task and transmits the data request scope for this computational task to the computation execution node corresponding to each related business department. Cross-departmental computational sub-tasks are retained in the coordination and control node.

[0050] a3: The calculation execution node executes the received single-department calculation sub-tasks within the scope of the data request according to the business logic corresponding to the collaborative calculation request. After the calculation is completed, it only submits the calculation results to the coordination and control node.

[0051] a4: Based on the business logic corresponding to the collaborative computing request, receive the computing results of the computing sub-tasks returned by each computing execution node; at the same time, execute cross-departmental computing sub-tasks in the coordination and control node.

[0052] a5: The coordination and control node transmits the required feedback data to the requester based on the computation request.

[0053] In step a2, when the coordination control node sends the task to the computing execution node, the specific steps include: d1: The coordination and control node generates a separate instruction for each single-department computational subtask; The instructions include: basic information, data parameters, calculation parameters, and security parameters; Basic information includes: subtask ID and subtask name; computational parameters include: the computational task model included in the subtask; data parameters include: the data identifier and data request range of the input parameters in the computational task model; security parameters include: encryption algorithm type and signature algorithm type. d2: The coordination and control node confirms the business department corresponding to the single-department calculation subtask, and records the calculation execution node of the business department as the target node; d3: The coordinating control node encrypts the instruction content according to security parameters and then distributes it to the target node through a secure channel.

[0054] The complete execution process is described below based on Example 3. Example 3 is for calculating clues about illegally modified vehicles.

[0055] I. Constructing a collaborative computing system; All participating business departments negotiate to build a coordination and control node; the market supervision department, traffic management department, and electric bicycle sales stores configure their respective data providing nodes and computing execution nodes, register their own computing execution nodes to the coordination and control node, and perform node initialization operations.

[0056] Considering that this task is a secure set intersection operation, the Secure Set Intersection (PSI) protocol based on scrambling circuits is selected.

[0057] II. Analysis Phase.

[0058] Analyze each existing collaborative governance business involving cross-departmental data sharing to obtain the business logic and the computational tasks and business data included in the business logic; In this embodiment, the business departments involved include: market supervision departments, traffic management departments, and electric bicycle sales stores.

[0059] The main business logic is as follows: Market supervision departments, in conjunction with traffic management departments and electric bicycle sales stores, analyze data such as section speed measurement, vehicle brand and model, and the store where the vehicle was purchased to obtain clues about illegal modifications of electric bicycles.

[0060] The traffic management department's calculation sub-task is as follows: Calculate the average speed V of the target e-bike as it travels through road segment AB. The calculation formula is as follows: V=L / (t Be -t Ae ) =4km / 6min=4km / 0.1h=40km / h.

[0061] After the calculation is completed, a list of license plate number HM (123456), V (40 km / h), and vehicle identification number (1757220**667922) is generated.

[0062] The calculation subtasks of the market supervision department are as follows: To obtain clues about illegal modifications of electric bicycles, based on data such as the maximum designed speed (e.g., 25 km / h) it has, the traffic management department is queried for vehicle identification numbers (1757220**667922) of vehicles with a speed greater than or equal to 25 km / h. Through the vehicle identification number (1757220**667922), information such as vehicle brand, model, sales store ID, and location is associated and queried from the sales stores.

[0063] The business data that the traffic management department needs to collect includes: For speeding records, characteristic data such as vehicle identification number ID, license plate number HM, number of speeding times n, and the maximum driving speed v collected on the road surface are extracted. For example, for the driving data of an electric bicycle, the target electric bicycle e-bike passes through two adjacent upstream and downstream intersections A and B; the distance L between intersection A and intersection B; the electric bicycle e-bike passes through intersection A at time t Ae , and passes through intersection B at time t Be ; the digital license plate number HM (123456) and vehicle identification number (1757220**667922) of the target electric bicycle e-bike.

[0064] The business data that the market supervision department needs to collect includes: For sales data, characteristic data such as vehicle identification number ID and maximum designed speed are extracted. For example, the maximum designed speed of vehicle A when it leaves the factory is 25 km / h, and its vehicle identification number is (1757220**667922).

[0065] The sales stores extract data such as sales brand, model, vehicle identification number ID, and sales time Tim. For example, sales brand (Xiaolu), model (X3), vehicle identification number ID (1757220**667922), and sales time Tim (January 4, 2025).

[0066] After the analysis is completed, the business logic, the calculation tasks included in the business logic, and the split calculation subtasks are all saved in the coordination control node.

[0067] III. Data collaborative sharing.

[0068] The market supervision department发起数据协同共享请求,明确本次的计算请求:需要查询交管部门的号牌号码、整车编码列表,以及销售门店的门店ID、销售时间、品牌型号等数据。

[0069] It should be noted that there is an incorrect expression in the original text of . It should be "The market supervision department发起数据协同共享请求" which is not a complete sentence. It is assumed that it should be "The market supervision department发起a data collaborative sharing request". The translation is adjusted accordingly.After receiving a data sharing request, the coordination and control node locates the internally stored business name: "Collaborative Governance Business for Calculating Clues of Illegally Modified Vehicles," and identifies the relevant business logic and computational subtasks. It then distributes these subtasks to the computational execution nodes of the market supervision department, traffic management department, and sales outlets. These nodes retrieve the necessary data from the data provider node and encrypt the data using a homomorphic encryption algorithm, employing the Paillier encryption algorithm.

[0070] After the market supervision department, traffic management department and sales store complete the calculation execution node, they obtain their respective encrypted result data and converge to the result verification node, which verifies the rationality of the calculation result.

[0071] After the calculation is complete, the result verification node encrypts the result using the market supervision department's public key and returns it to the market supervision department. The market supervision department then decrypts the result using its own private key to obtain the final data sharing result. The output format is as follows. Figure 3 As shown.

Claims

1. A method for collaborative data sharing of electric bicycles based on multi-party secure computation, characterized in that, It includes the following steps: S1: Analyze each existing collaborative governance business involving cross-departmental data sharing to obtain the business logic and the computational tasks included in the business logic; The business logic describes all the business data used in each collaborative governance business and the relationships between the business data. The relationships between the business data constitute the computing tasks. S2: The computation task is split into computation subtasks; Name each computational subtask and generate a unique subtask ID; The computational subtasks include: single-department computational subtasks and cross-departmental computational subtasks; each computational subtask includes N computational task models, where N≥1; The single-department calculation subtask is a calculation task that uses only data provided by a single business department during the calculation process; each single-department calculation subtask type is assigned a unique associated business department; The cross-departmental computing sub-task is one in which the data used in the computing process is provided by more than one business department; the cross-departmental computing sub-task does not have a corresponding business department. S3: Build a collaborative data computing system; The collaborative computing system includes: a coordination control node and computing execution nodes; the number of computing execution nodes is greater than or equal to 1; each computing execution node is communicatively connected to the coordination control node. The coordination and control node exists independently of the business departments; The business logic and all computational subtasks corresponding to each of the aforementioned collaborative governance services are stored in the coordination and control node; At least one computing execution node is assigned to each business unit, and the computing execution node communicates with the data providing node of the business unit. S4: Each business unit submits a calculation request for this collaborative governance business to the collaborative control node; The computation request includes: the name of the collaborative governance service, the requested data, the scope of the data request, and the purpose of use; The requested data is the business data that the requesting party wishes to obtain through the calculations of this collaborative governance business; the requested data includes all or part of the output data of each department in the collaborative governance business; The data request scope is used to specify the time range, geographical range and other restrictions for all business departments to collect raw data uniformly. The stated purpose of use is used to describe the reason for requesting the data; S5: Upon receiving a collaborative computing request, perform the following operations in the collaborative computing system: a1: The coordination and control node confirms the associated business department involved in each of the computing requests; a2: The coordination and control node, based on the single-department calculation sub-tasks involved in the calculation request, assigns corresponding calculation tasks to the calculation execution nodes corresponding to each associated business department and transmits the data request range of this calculation task. The cross-departmental computing subtasks are retained in the coordination and control node; a3: The computing execution node executes the received single-department computing sub-task within the scope of the data request according to the business logic corresponding to the collaborative computing request, and only submits the computing result to the coordination control node after the computing is completed; a4: Based on the business logic corresponding to the collaborative computing request, receive the computing results of each computing execution node's returned computing sub-task; and simultaneously execute the cross-departmental computing sub-task in the coordination control node; a5: The collaborative computing system will transmit the requested data that needs to be fed back to the requesting party.

2. The method for collaborative data sharing of electric bicycles based on multi-party secure computation according to claim 1, characterized in that: The collaborative computing system also includes: a result verification node; The result verification node is communicatively connected to the coordination and control node; The coordination and control node sends the request data that needs to be fed back to the result verification node; the result verification node verifies the request data that needs to be fed back based on business logic requirements and judges the rationality of the data; If the data meets the business logic requirements, it will be returned to the requester. If data does not meet the requirements of the business logic, further processing will be carried out according to the specific circumstances.

3. The method for collaborative sharing of electric bicycle data based on multi-party secure computation according to claim 1, characterized in that: In step S1, there is also Includes the following operations: Build a computational task model based on collaborative governance business; The computational task model includes: computational model name, computational model ID, input parameters, computational logic, and output parameters; The input parameters include the data that the computation task needs to process, including: data type, data description, and data unit; The computational logic describes the core computational logic of the computational task, and defines the computational method for each input parameter, including: operation, transformation and / or logical judgment; The output parameters are the final results obtained after the input parameters are processed according to the calculation logic. The output parameters include: data type, data description, and data unit.

4. The method for collaborative sharing of electric bicycle data based on multi-party secure computation according to claim 1, characterized in that: The business units include: traffic management departments, market supervision departments, and vehicle-using companies; the vehicle-using companies include: express delivery and food delivery companies and car-sharing rental companies.

5. The method for collaborative sharing of electric bicycle data based on multi-party secure computation according to claim 4, characterized in that: Step S1 also includes: b1: Define the raw data types that each business department is responsible for collecting related to business processing, and define a unique data identifier for each raw data type; The raw data collected by the traffic management department includes: static traffic management data and dynamic traffic management data; the static traffic management data includes: electric bicycle product name, model, manufacturer, production plant, vehicle identification number, motor number, vehicle weight, maximum design speed, motor power, and license plate registration information; the license plate registration information includes: owner information such as name, contact information, and address, license plate number, and license plate type; the dynamic traffic management data includes: electric bicycle travel routes, violations, and accidents; The raw data collected by the market supervision department includes: product name, model, manufacturer, production plant, vehicle identification number, and sales outlet information; The raw data collected by the vehicle-using companies includes: rider's name, contact information, vehicle identification number (VIN) and license plate number of the electric bicycle used; b2: Standardize data processing methods; The data standardization processing method includes: converting data according to a unified data format and standard.

6. The method for collaborative sharing of electric bicycle data based on multi-party secure computation according to claim 1, characterized in that: In the data collaborative computing system, each node performs the following operations after joining: c1: All nodes complete identity registration, generate unique identifiers, and register roles before deployment; c2: The coordination and control node generates the public parameters required for the multi-party secure computation protocol through a distributed key generation protocol; each node generates a local public-private key pair for encrypted communication and signing.

7. The method for collaborative data sharing of electric bicycles based on multi-party secure computation according to claim 1, characterized in that: In step a2, when the coordination control node sends the task to the computing execution node, the specific steps include: d1: The coordination and control node generates a separate instruction for each of the single-department computing subtasks; The instructions include: basic information, data parameters, calculation parameters, and security parameters; The basic information includes: subtask ID and subtask name; the calculation parameters include: the calculation task model included in the subtask; the data parameters include: the data identifier and data request range of the input parameters in the calculation task model; the security parameters include: encryption algorithm type and signature algorithm type. d2: The coordination and control node confirms the business department corresponding to the single-department calculation subtask, and records the calculation execution node of the business department as the target node; d3: The coordination and control node encrypts the instruction content according to the security parameters and then distributes it to the target node through a secure channel.

8. The method for collaborative sharing of electric bicycle data based on multi-party secure computation according to claim 1, characterized in that: Before step S5 is executed, the following steps also need to be performed in the collaborative computing system: e1: Select a multi-party secure computation protocol for the computational tasks executed in the system; The multi-party secure computation protocol includes: a secret-sharing-based protocol, a garbled circuit protocol, or an unintentional transmission protocol; e2: Select the corresponding homomorphic encryption algorithm for each computation request; The homomorphic encryption algorithms include: additive homomorphic Paillier, hierarchical homomorphic LHE, fully homomorphic FHE, and BFV homomorphic; The settings for the homomorphic encryption algorithm include: selecting only one, or selecting multiple based on classification encryption modes.

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