A privacy protection task allocation method based on beaver triple

Abstract

The application discloses a privacy protection task allocation method based on Beaver triples, privacy protection is realized for task participants and task data through secret sharing, and task allocation under privacy protection is realized through Beaver triples, so that suitable task participants can be selected to execute tasks without exposing privacy, that is, the privacy of users is protected, and the efficiency and effectiveness of the task allocation result can be ensured. The application realizes double-target optimization multi-task allocation, and can ensure that the task allocation result meets the effectiveness of two optimization targets under the premise of privacy protection.

Description

Technical Field

[0001] This invention relates to the technical field of information security and privacy protection, and in particular to a privacy protection task allocation method based on Beaver triples. Background Technology

[0002] In recent years, with the development of network communication technology and the popularization of smart terminal devices, the mobile Internet and the sharing economy have developed rapidly. Traditional network-based crowdsourcing models have further evolved into new data collection and service collaboration models such as Spatial Crowdsourcing (SC) and Mobile Crowdsensing (MCS). A typical SC / MCS system generally includes three main entities: task requesters, workers (participants), and a platform server. Task requesters publish tasks through the platform, the platform selects suitable workers to participate and execute the tasks based on the task requirements, and after completing the tasks, the workers upload the results to the platform, which then aggregates and provides feedback to the task requesters. Because this model is widely used in scenarios such as food delivery, ride-hailing, geographic data sharing, environmental monitoring, and intelligent transportation, spatiotemporally sensitive data such as task and worker location information, movement trajectories, and preferences are frequently collected and processed during system operation. Without effective protection, this can easily lead to problems such as worker privacy leaks, decreased user willingness, and economic losses for task requesters. Therefore, achieving privacy protection while ensuring service quality has become an important research direction in the SC / MCS field.

[0003] In practical applications, SC / MCS increasingly presents complex scenarios involving concurrent multi-tasks and multiple requesters. Task allocation can generally be divided into offline allocation and online allocation: offline allocation performs global optimization under the condition of known task and worker location / trajectory information, while online allocation requires real-time collection of location information and dynamic decision-making. Meanwhile, researchers are also focusing on multi-task scenarios, such as multi-task allocation, multi-task data aggregation, and multi-task incentive mechanisms. Based on the number of requesters and tasks, existing data allocation methods in MCS can be divided into three categories: single-task allocation (one task and multiple task workers) and multi-task allocation (multiple tasks and multiple task workers). In recent years, esoteric techniques such as differential privacy and homomorphic encryption have been widely used for privacy protection in task allocation within swarm intelligence sensing scenarios. Differential privacy technology adds noise to sensitive data to desensitize it, while homomorphic encryption, through its homomorphic computation properties, performs computation on encrypted data, preventing data from being directly exposed to the sensing platform and achieving data privacy protection.

[0004] However, current differential privacy schemes suffer from reduced effectiveness of task allocation results, while homomorphic encryption suffers from significant computational and communication overhead. Furthermore, current privacy-preserving task allocation schemes primarily focus on single-objective task allocation, with limited research on privacy protection in multi-objective, multi-task allocation scenarios. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a privacy-preserving task allocation method based on Beaver triples. This method protects privacy through secret sharing and calculates privacy data based on Beaver triples. It can perform calculations on the encrypted data uploaded by the task requester (TR) and worker (TP) under privacy protection, including calculating the distance between the worker and the task, the effective time for the worker to perform the task, and the matching degree between the worker and the task. Through the designed task allocation algorithm, it achieves the optimal task allocation result under constraints in a multi-objective, multi-task allocation scenario.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A privacy-preserving task allocation method based on Beaver triples includes the following steps:

[0008] S1. Sensing Platform Initialization: Performed by the sensing platform server. Generate and publish public parameters for two independent sensing platform servers. and server Complete registration and generate Beaver triples using the Paillier homomorphic encryption algorithm;

[0009] S2, Task Issuance: The task requester uses a secret sharing method to split the perception task into two task shares and sends them to the server separately. and server ;

[0010] S3. Worker Uploads Information: Workers use a secret sharing method to split their personal information into two parts and send them separately to the server. and server ;

[0011] S4. Data computation under privacy protection: Server and server A secure computing module is built based on Beaver triples. Based on information share, the secure computing module is used to calculate the worker-task matching degree, the average cost of the worker to complete the task, the distance between the worker and the task, and the effective time of the worker to execute the task in the dense state.

[0012] S5. Multi-objective multi-task allocation: The perception platform, based on the average cost of workers completing tasks, the matching degree between workers and tasks, and the effective time of workers executing tasks, obtains a task allocation result that satisfies the minimum total cost of workers executing tasks and the maximum value created by workers through a safe comparison protocol and combined with the NSGA-II genetic algorithm.

[0013] S6. Receive and execute tasks: Server and server After obtaining the task allocation results, the corresponding task share is sent to the worker who was assigned the task. The worker then uses the task share to restore the complete task information and execute the task.

[0014] Furthermore, step S1 includes the following steps:

[0015] S1.1, Initialization of the sensing platform:

[0016] The sensing platform specifies 2048 security bits and randomly generates a large prime number that meets preset conditions; based on the large prime number, it calculates the public key and private key parameters, generates the public key, and sends it to the server. Define auxiliary calculation functions and calculate the private key to complete platform initialization;

[0017] S1.2. Generate Beaver triples based on the Paillier homomorphic encryption algorithm:

[0018] By server and server Generate the corresponding common parameters respectively; by the server After encrypting its public parameters, the encrypted parameters are obtained and sent to the server. By server Generate random numbers, calculate temporary auxiliary parameters, and send them back to the server. By server ,server The secret sharing form of Beaver triples is obtained by calculating based on temporary auxiliary parameters and random numbers; this step is repeated to generate multiple Beaver triples.

[0019] Furthermore, step S2 includes the following steps:

[0020] S2.1 All task requesters send task requests to the perception platform;

[0021] S2.2 Represent the task requester's task as a data structure that includes the latitude and longitude of the task execution location, the task end time, the task attributes, and the task budget;

[0022] S2.3 The task requester uses a secret sharing method to split the task into a first task share and a second task share, and sends them to the server separately. and server Each task share includes the latitude and longitude of the corresponding task execution location, task end time, task attributes, and task budget.

[0023] Furthermore, step S3 includes the following steps:

[0024] S3.1 All workers send their personal information to the sensing platform;

[0025] S3.2 Represent worker personal information as a data structure that includes worker location latitude and longitude, worker movement speed, worker task reward, worker task cost, and worker ability;

[0026] S3.3 Workers use a secret sharing method to split personal information into a first information share and a second information share, and send them to the server separately. and server Each information share includes the corresponding worker's location latitude and longitude, movement speed, task reward, task cost, and worker's ability after being split.

[0027] Furthermore, the security calculation module includes a distance calculation submodule and a division submodule;

[0028] The distance calculation submodule specifically performs the following operations:

[0029] server and server Each entity holds a secret shared share of worker and task positions; a secure comparison protocol is built based on Beaver triples to complete share comparison and locally calculate the secret sharing of the difference; the secret sharing form of the absolute value of the difference is calculated through a secure multiplication protocol; server and server The secret sharing share of the Manhattan distance is calculated based on the absolute value of the difference.

[0030] The division submodule performs the following operations:

[0031] server and server Each entity holds a secret shared share of worker and task locations, while the worker holds auxiliary parameters; the server... Select a random number and calculate temporary auxiliary parameters locally, then send it to the server. ;server Temporary auxiliary calculation parameters are calculated based on temporary auxiliary parameters and random numbers and sent back to the worker; the worker calculates temporary parameters based on the temporary auxiliary calculation parameters and sends them to the server. ,server The division calculation result is obtained based on temporary parameters and random numbers.

[0032] Furthermore, in step S4, based on the additive secret sharing property, the server... and server The secret-shared share of the average cost for each worker to complete a task is calculated, and the final value of the average cost is obtained through data interaction.

[0033] Based on the Beaver triplet, a secure multiplication protocol is implemented. The worker capability vector and the task attribute vector are multiplied by a 0 / 1 dot product to obtain the dot product secret shared share. The worker-task matching degree is obtained through data interaction.

[0034] Based on the distance calculation submodule, the distance between the worker and the task is calculated according to the secret shared share of the worker's position and the task's position; based on the worker's speed and the secret shared share of the task's end time, the division submodule performs a division operation, and then the secret shared subtraction is performed to restore the share result, so as to obtain the effective time for the worker to perform the task.

[0035] Furthermore, step S5 includes the following steps:

[0036] S5.1 Set optimization objectives: minimize the total cost of workers performing tasks and maximize the value created by workers performing tasks; set constraints: the effective time of workers performing tasks is greater than 0, the reward is less than the task budget, the worker-task matching degree is ≥1, and each worker is only assigned one task;

[0037] S5.2. A chromosome is used to represent the task assignment result of a single worker. A chromosome value of -1 indicates that the worker has not been assigned a task, and other values ​​indicate that the worker has been assigned to the corresponding numbered task. First, the original population is randomly generated and initialized. Constraint judgment and correction are performed on the chromosome. Multiple sets of non-dominated fronts are calculated and individuals at each level are sorted by crowding distance. Based on the front level and crowding distance, a binary tournament selection is performed to obtain parent individuals. Offspring individuals are generated through crossover and mutation operations. Constraint judgment and correction are performed again to ensure that the offspring are all feasible solutions. After multiple rounds of iteration, a set of Pareto solutions is obtained. The points on the Pareto front are connected in the normalized target space. The point with the largest perpendicular distance to the endpoint is selected as the compromise solution, which is the final task assignment result.

[0038] Furthermore, step S6 includes the following steps:

[0039] S6.1, by server The task allocation result obtained in step S5 is sent to the server via a secure channel. ;

[0040] S6.2, Server and server The two corresponding task shares are sent to the workers who were assigned the task via a secure channel.

[0041] S6.3 After receiving two task shares, the worker restores the complete task information through a secret shared share reconstruction method and executes the task. After the task is completed, the worker sends the task execution report to the server. .

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] 1. This invention achieves multi-task data encrypted computation by using secret sharing and Beaver triples, enabling homomorphic computation in an encrypted state. Compared with the existing Paillier homomorphic encryption method, it has lower computational overhead and communication.

[0044] 2. This invention ensures the privacy of each worker and task requester by secretly sharing information, thus protecting the privacy of task requesters and workers and ensuring that the platform cannot obtain personal privacy information.

[0045] 3. This invention realizes the allocation of dual objectives and multiple tasks, which can ensure the validity of task allocation results under the premise of privacy protection, while ensuring that the computational overhead is within an acceptable range, and can achieve task allocation more efficiently. Attached Figure Description

[0046] Figure 1 A conceptual model diagram for assigning tasks to a single task.

[0047] Figure 2 A conceptual model diagram for multi-task allocation.

[0048] Figure 3 This is a flowchart of the method of the present invention.

[0049] Figure 4 This is a schematic diagram of the system model of the present invention.

[0050] Figure 5 This is a schematic diagram of the system operation of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0052] Terminology Explanation: Secret Sharing: Secret sharing is a cryptographic technique that divides sensitive data into multiple "shares" and distributes them to multiple participants for safekeeping. A single participant holding only one share cannot recover the original data; the original data can only be reconstructed when a preset threshold number of shares are combined. Secret sharing is commonly used in scenarios such as secure multi-party computation, enabling multiple participants to complete collaborative computations without revealing their individual private inputs.

[0053] Beaver Triples: Beaver Triples are a type of preprocessed random data used in secure multi-party computation to efficiently perform multiplication operations. They are typically represented as a set of three random numbers (or their shared form) (a, b, c), where c = a ⋅ b. Participants can pre-generate and distribute a secret shared share of this triple during the offline phase. During the online computation phase, this triple is used to transform "multiplication" into a small number of additions and a single interaction, thereby completing the product calculation without revealing the multiplication input, improving computational efficiency and reducing communication overhead.

[0054] Homomorphic encryption is an encryption technique that allows specific operations to be performed directly on ciphertext. The resulting ciphertext after performing the operation on the ciphertext will have the same decryption result as the plaintext. With homomorphic encryption, data providers can provide ciphertext to a third party or computational entity for operations such as addition and multiplication without disclosing the plaintext data, thus achieving verifiable or usable encrypted computation capabilities while protecting data privacy.

[0055] like Figure 1 , Figure 2 As shown, based on the number of task requesters and tasks, existing task allocation methods in MCS can be divided into two categories: single-task allocation (one task and multiple task managers) and multi-task allocation (multiple tasks and multiple task managers). This embodiment provides a privacy-preserving task allocation method based on Beaver triples, implementing the second type: privacy-preserving multi-task allocation in MCS. It allocates task and worker information through secret sharing, achieving data security on the perception platform. Furthermore, it uses Beaver triples to assist in homomorphic computation in encrypted states and employs a designed task allocation algorithm to assign tasks to appropriate workers.

[0056] This embodiment includes the following entities: task requester, sensing platform, and worker. The following is a description of each entity:

[0057] Task requesters: This group consists of multiple requesters who initiate multiple tasks. They have task awareness needs in different regions or time periods, therefore they need to send task requests to the awareness platform. In this embodiment, each requester first allocates their task into two shares, and then sends them to two servers of the awareness platform respectively.

[0058] Perception Platform: The perception platform is responsible for receiving tasks from requesters and distributing them to workers. Based on the information from both the requester and the worker, the perception platform uses a secure computation protocol and task allocation algorithm to obtain the task allocation result, and then sends the task information to the corresponding worker according to the allocation result.

[0059] Workers: Responsible for completing multiple tasks issued by the sensing platform. Workers secretly share and upload information to the server, obtain task information from the server, and then execute the tasks based on the task information.

[0060] like Figures 3 to 5 As shown, the specific steps of the above-mentioned privacy protection task allocation method based on Beaver triples are as follows:

[0061] S1. Sensing Platform Initialization: Performed by the sensing platform server. Generate and publish public parameters for two independent sensing platform servers. and server Complete registration and generate Beaver triples using the Paillier homomorphic encryption algorithm; the specific steps include:

[0062] S1.1, Initialization of the sensing platform:

[0063] First, the sensing platform specifies 2048 security bits and randomly generates bits that meet preset conditions. large prime numbers and ;

[0064] According to large prime numbers and Calculate public key parameters and private key parameters As shown in the following formula:

[0065] ;

[0066] ;

[0067] Select public key parameters According to the public key parameter and public key parameters Generate public key , and send to the server ;

[0068] Define auxiliary calculation function And calculate the private key As shown in the following formula:

[0069] ;

[0070] ;

[0071] ;

[0072] Initialization complete;

[0073] S1.2. Generate Beaver triples based on the Paillier homomorphic encryption algorithm:

[0074] By server Generate corresponding common parameters , By server Generate corresponding common parameters , ;

[0075] By analyzing public parameters , Encryption yields encryption parameters , And send to the server ;

[0076] Then by the server Generate a random number r and calculate temporary auxiliary parameters. The calculation results are then sent to the server. As shown in the following formula:

[0077] ;

[0078] By server Based on temporary auxiliary parameters Calculation parameters As shown in the following formula:

[0079] ;

[0080] By server Parameters are calculated based on the random number r. As shown in the following formula:

[0081] ;

[0082] Thus, the secret sharing form of the Beaver triple is obtained. and ;

[0083] Furthermore, the parameters of the secret-sharing form satisfy the definition of a Beaver triple, as shown in the following equation:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] Repeat this step to generate multiple Beaver triples.

[0089] S2, Task Issuance: The task requester uses a secret sharing method to split the perception task into two task shares and sends them to the server separately. and server The sensing platform guides worker selection and task allocation; specifically, it includes the following steps:

[0090] S2.1, by all task requesters Send a task request to the sensing platform;

[0091] S2.2, the task requester The task request is recorded as ,in, , These are the latitude and longitude coordinates of the mission execution location. The task completion time. For task attributes, Budget for the task;

[0092] S2.3, by the task requester Its task is split into first task shares through a secret sharing method. Second task share and sent to the server respectively. and server ,in, and These are the latitude and longitude coordinates of the task execution locations after being split up. and These are the completion times of the split tasks. and These are the task attributes that are split. and These are the budgets for the tasks after they have been split.

[0093] S3. Worker Uploads Information: Workers use a secret sharing method to split their personal information into two parts and send them separately to the server. and server Specifically, it includes the following steps:

[0094] S3.1, by all workers Send personal information to the sensing platform;

[0095] S3.2, to the workers Personal information record ,in, , The latitude and longitude of the worker's location. For the worker's movement speed, The payment a worker receives for completing a task. The cost of having a worker complete a task. For the workers' capabilities;

[0096] S3.3, by workers His personal information was divided into first information shares through secret sharing. Second information share and sent to the server respectively. and server ,in and These are the latitude and longitude coordinates of the workers' locations after the division. and These represent the worker movement speeds after the division. and The payment for each worker who completes a task after being divided into groups. and The cost for each worker to complete a task after the division of labor. and These refer to the worker capabilities after the breakdown.

[0097] S4. Data computation under privacy protection: Server and server A secure computation module is constructed based on Beaver triples. Based on information shares, the secure computation module calculates the worker-task matching degree, the average cost of a worker completing a task, the distance between the worker and the task, and the effective time for a worker to execute a task in a dense state. The secure computation module includes a distance calculation submodule and a division submodule.

[0098] The distance calculation submodule specifically performs the following operations:

[0099] For position parameters , and position parameters , ,server and server Each holding a secret shared share , , , and , , , ,in, , , , ;

[0100] server and server Constructing a secure comparison protocol SecCmp using Beaver triples , , , ) and SecCmp( , , , ), and calculate , and , ,in, for and The secret shared share of the first comparison result; for and The secret shared share of the second comparison results; for and The secret shared share of the first comparison result; for and The secret shared share of the second comparison results;

[0101] server and server Each party subtracts from their local share, and the resulting difference is secretly shared. , and , ;

[0102] By server and server Construct preset parameters respectively , and , And calculate using the safe multiplication protocol and This leads to the secret sharing form of the absolute value of the difference. , and , ,in, and They are respectively and The absolute value of the difference The secret sharing form, and They are respectively and The absolute value of the difference Secret sharing format;

[0103] By server According to the form of secret sharing and The secret shared share of the Manhattan distance was calculated. ;

[0104] By server According to the form of secret sharing and The secret shared share of the Manhattan distance was calculated. ;

[0105] The division submodule performs the following operations:

[0106] server and server Each holding a secret shared share , ,and , Workers hold auxiliary parameters ,in, , ;

[0107] By server Select a random number r within the plaintext space range, and calculate temporary auxiliary parameters locally based on the random number r. , and send temporary auxiliary parameters And a random number r is given to the server ;

[0108] By server Based on temporary auxiliary calculation parameters And the random number r is used to calculate temporary auxiliary calculation parameters locally. , Thus, temporary auxiliary calculation parameters are obtained. , and temporary auxiliary calculation parameters Send to the workers;

[0109] Workers calculate parameters based on temporary auxiliary parameters Calculate temporary parameters , and send to the server ;

[0110] By server According to temporary parameters The calculation result is obtained by combining the random number r. .

[0111] The secure calculation module performs the following calculations in a dense state: worker-task matching degree, average cost for worker to complete task, distance between worker and task, and effective time for worker to execute task.

[0112] Based on the additive secret sharing property, by the server and server Calculate the secret sharing share of the average cost per worker to complete the task. and , where n is the number of workers;

[0113] By server Will Send to server The final value of average cost is obtained through data interaction. ;

[0114] A secure multiplication protocol based on Beaver triples is used to perform a dot product multiplication of the 0 / 1 values ​​of the worker capability vector and the task attribute vector to obtain the secret shared share of the dot product of the capability vector and the task vector. and Then, through data interaction, the matching degree between workers and tasks can be obtained;

[0115] Based on the distance calculation submodule, the distance between the worker and the task is calculated according to the secret shared shares of the worker's location and the task execution location. Based on the secret shared shares of the worker's speed and the task's end time, a division operation is performed through the division submodule. Finally, a secret shared subtraction is performed, and the effective time for the worker to execute the task is recovered from the secret shared shares of the result. As shown in the following formula:

[0116] .

[0117] S5. Multi-objective, multi-task allocation: Based on the average cost of workers completing tasks, the worker-task matching degree, and the effective time of workers executing tasks, the perception platform uses a safety comparison protocol combined with the NSGA-II genetic algorithm to obtain a task allocation result that minimizes the total cost of workers executing tasks and maximizes the value created by workers; specifically, it includes the following steps:

[0118] S5.1 Define the optimization objective as minimizing the total cost of workers performing tasks and maximizing the value created by workers performing tasks, as shown in the following formula:

[0119] ;

[0120] ;

[0121] in, For the matching degree between workers and tasks, The effective time for workers to perform tasks; The total cost of performing tasks for all workers assigned tasks. The value created by all workers in completing their assigned tasks;

[0122] The constraints are defined as follows: the worker's effective task execution time is greater than 0, the worker's compensation is less than the task budget, the worker-task matching degree is ≥1, and a single worker can only be assigned one task, as shown in the following formula:

[0123] , , , ;

[0124] in, This indicates the task assignment result for workers, where 1 indicates that worker i is assigned to task j, and 0 indicates that no task is assigned. Indicates the task budget; It indicates payment of workers' wages;

[0125] S5.2. A chromosome is used to represent the task assignment result of a single worker. A chromosome value of -1 indicates that the worker has not been assigned a task, and other values ​​indicate that the worker has been assigned to the corresponding numbered task. First, the original population is randomly generated and initialized. Constraint judgment and correction are performed on the chromosome. Multiple sets of non-dominated fronts are calculated and individuals at each level are sorted by crowding distance. Based on the front level and crowding distance, a binary tournament selection is performed to obtain parent individuals. Offspring individuals are generated through crossover and mutation operations. Constraint judgment and correction are performed again to ensure that the offspring are all feasible solutions. After multiple rounds of iteration, a set of Pareto solutions is obtained. The points on the Pareto front are connected in the normalized target space. The point with the largest perpendicular distance to the endpoint is selected as the compromise solution, which is the final task assignment result.

[0126] S6. Receive and execute tasks: Server and server After obtaining the task allocation results, the corresponding task share is sent to the worker assigned the task. The worker then uses the task share to restore the complete task information and execute the task. Specifically, this includes the following steps:

[0127] S6.1, by server The task allocation result obtained in step S5 is sent to the server via a secure channel. ;

[0128] S6.2, Server and server Share the first task share via secure channel Second task share Send it to the worker assigned the task;

[0129] S6.3 After receiving two task shares, the worker restores the complete task information through a secret, shared share reconstruction method. It then executes the task, and upon completion, sends a task execution report to the server. .

[0130] Based on this invention, users can ensure the security of data on the collective sensing task allocation platform, guarantee that the collective sensing server will not obtain users' sensitive information, and protect the privacy and security of task submitters and workers.

[0131] This invention, while considering privacy protection in task allocation, achieves a more efficient task allocation scheme. Users can ensure data security through secret sharing, preventing malicious users from obtaining their data. A secure computation module built using Beaver triples enables data computation under privacy protection, offering advantages over other homomorphic encryption schemes in terms of lower computational and communication overhead. Furthermore, the NSGA-II genetic algorithm enables efficient allocation of dual-objective optimization tasks.

[0132] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A privacy-preserving task allocation method based on Beaver triples, characterized in that, Including the following steps: S1. Sensing Platform Initialization: Performed by the sensing platform server. Generate and publish public parameters for two independent sensing platform servers. and server Complete registration and generate Beaver triples using the Paillier homomorphic encryption algorithm; S2, Task Issuance: The task requester uses a secret sharing method to split the perception task into two task shares and sends them to the server separately. and server ; S3. Worker Uploads Information: Workers use a secret sharing method to split their personal information into two parts and send them separately to the server. and server ; S4. Data computation under privacy protection: Server and server A secure computing module is built based on Beaver triples. Based on information share, the secure computing module is used to calculate the worker-task matching degree, the average cost of the worker to complete the task, the distance between the worker and the task, and the effective time of the worker to execute the task in the dense state. S5. Multi-objective multi-task allocation: The perception platform, based on the average cost of workers completing tasks, the matching degree between workers and tasks, and the effective time of workers executing tasks, obtains a task allocation result that satisfies the minimum total cost of workers executing tasks and the maximum value created by workers through a safe comparison protocol and combined with the NSGA-II genetic algorithm. S6. Receive and execute tasks: Server and server After obtaining the task allocation results, the corresponding task share is sent to the worker who was assigned the task. The worker then uses the task share to restore the complete task information and execute the task.

2. The privacy-preserving task allocation method based on Beaver triples according to claim 1, characterized in that, Step S1 includes the following steps: S1.1, Initialization of the sensing platform: The sensing platform specifies 2048 security bits and randomly generates a large prime number that meets preset conditions; based on the large prime number, it calculates the public key and private key parameters, generates the public key, and sends it to the server. Define auxiliary calculation functions and calculate the private key to complete platform initialization; S1.

2. Generate Beaver triples based on the Paillier homomorphic encryption algorithm: By server and server Generate the corresponding common parameters respectively; by the server After encrypting its public parameters, the encrypted parameters are obtained and sent to the server. By server Generate random numbers, calculate temporary auxiliary parameters, and send them back to the server. By server ,server The secret sharing form of Beaver triples is obtained by calculating based on temporary auxiliary parameters and random numbers; this step is repeated to generate multiple Beaver triples.

3. The privacy-preserving task allocation method based on Beaver triples according to claim 1, characterized in that, Step S2 includes the following steps: S2.1 All task requesters send task requests to the perception platform; S2.2 Represent the task requester's task as a data structure that includes the latitude and longitude of the task execution location, the task end time, the task attributes, and the task budget; S2.3 The task requester uses a secret sharing method to split the task into a first task share and a second task share, and sends them to the server separately. and server Each task share includes the latitude and longitude of the corresponding task execution location, task end time, task attributes, and task budget.

4. The privacy-preserving task allocation method based on Beaver triples according to claim 1, characterized in that, Step S3 includes the following steps: S3.1 All workers send their personal information to the sensing platform; S3.2 Represent worker personal information as a data structure that includes worker location latitude and longitude, worker movement speed, worker task reward, worker task cost, and worker ability; S3.3 Workers use a secret sharing method to split personal information into a first information share and a second information share, and send them to the server separately. and server Each information share includes the corresponding worker's location latitude and longitude, movement speed, task reward, task cost, and worker's ability after being split.

5. The privacy-preserving task allocation method based on Beaver triples according to claim 1, characterized in that, The security calculation module includes a distance calculation submodule and a division submodule; The distance calculation submodule specifically performs the following operations: server and server Each entity holds a secret shared share of worker and task positions; a secure comparison protocol is built based on Beaver triples to complete share comparison and locally calculate the secret sharing of the difference; the secret sharing form of the absolute value of the difference is calculated through a secure multiplication protocol; server and server The secret sharing share of the Manhattan distance is calculated based on the absolute value of the difference. The division submodule performs the following operations: server and server Each entity holds a secret shared share of worker and task locations, while the worker holds auxiliary parameters; the server... Select a random number and calculate temporary auxiliary parameters locally, then send it to the server. ;server Temporary auxiliary calculation parameters are calculated based on temporary auxiliary parameters and random numbers and sent back to the worker; the worker calculates temporary parameters based on the temporary auxiliary calculation parameters and sends them to the server. ,server The division calculation result is obtained based on temporary parameters and random numbers.

6. The privacy-preserving task allocation method based on Beaver triples according to claim 5, characterized in that, In step S4, based on the additive secret sharing property, the server... and server The secret-shared share of the average cost for each worker to complete a task is calculated, and the final value of the average cost is obtained through data interaction. Based on the Beaver triplet, a secure multiplication protocol is implemented. The worker capability vector and the task attribute vector are multiplied by a 0 / 1 dot product to obtain the dot product secret shared share. The worker-task matching degree is obtained through data interaction. Based on the distance calculation submodule, the distance between the worker and the task is calculated according to the secret shared share of the worker's position and the task's position; based on the worker's speed and the secret shared share of the task's end time, the division submodule performs a division operation, and then the secret shared subtraction is performed to restore the share result, so as to obtain the effective time for the worker to perform the task.

7. The privacy-preserving task allocation method based on Beaver triples according to claim 1, characterized in that, Step S5 includes the following steps: S5.1 Set optimization objectives: minimize the total cost of workers performing tasks and maximize the value created by workers performing tasks; set constraints: the effective time of workers performing tasks is greater than 0, the reward is less than the task budget, the worker-task matching degree is ≥1, and each worker is only assigned one task; S5.

2. A chromosome is used to represent the task assignment result of a single worker. A chromosome value of -1 indicates that the worker has not been assigned a task, and other values ​​indicate that the worker has been assigned to the corresponding numbered task. First, the original population is randomly generated and initialized. Constraint judgment and correction are performed on the chromosome. Multiple sets of non-dominated fronts are calculated and individuals at each level are sorted by crowding distance. Based on the front level and crowding distance, a binary tournament selection is performed to obtain parent individuals. Offspring individuals are generated through crossover and mutation operations. Constraint judgment and correction are performed again to ensure that the offspring are all feasible solutions. After multiple rounds of iteration, a set of Pareto solutions is obtained. The points on the Pareto front are connected in the normalized target space. The point with the largest perpendicular distance to the endpoint is selected as the compromise solution, which is the final task assignment result.

8. The privacy-preserving task allocation method based on Beaver triples according to claim 1, characterized in that, Step S6 includes the following steps: S6.1, by server The task allocation result obtained in step S5 is sent to the server via a secure channel. ; S6.2, Server and server The two corresponding task shares are sent to the workers who were assigned the task via a secure channel. S6.3 After receiving two task shares, the worker restores the complete task information through a secret shared share reconstruction method and executes the task. After the task is completed, the worker sends the task execution report to the server. .

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