Identity privacy protection-based trusted worker recruitment method

Abstract

The application discloses a trusted worker recruitment method based on identity privacy protection and relates to the fields of trusted computing and quality guarantee data collection of a crowd intelligence network. The network structure is composed of a data requester, a server, a reputation authentication center (TA) and workers. The workers obtain pseudonyms by interacting with the TA in advance, and the pseudonyms are used in the data interaction process to protect privacy. The reputation information of the workers is stored in the TA, and the encrypted reputation degree is taken when applying for a task. Then, the server adopts a two-party reputation verification process based on zero-knowledge proof, so that the server can verify whether the reputation submitted by the worker is true without knowing the reputation value of the worker. Then, the server selects N workers to collect data. After the data is collected, the server calculates the true value by adopting a true value calculation method to provide the data requester with the true value, compares and calculates the reputation degrees of the workers, and updates the reputation degrees of the workers through the TA.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of trusted computing, quality assurance data collection of crowd intelligence network, more particularly to a trusted worker recruitment method based on identity privacy protection. BACKGROUND

[0002] Mobile crowdsensing (MCS) as a new data collection technology, uses mobile devices carrying sensors and a large number of user groups to complete the sensing task, and has shown great application potential in military, industry, health, environmental monitoring and other fields. However, the wide application of crowdsensing technology is faced with the problems of privacy protection and low data quality.

[0003] In the mobile crowdsensing network, the process of data sensing is as follows: data requesters, namely DR, publish the sensing data tasks to be done by issuing to the platform; workers check the published tasks through the platform; if the worker is willing to perform the task, he applies to the platform to do the task; the platform selects the worker and notifies the selected worker to perform the task; the worker submits the sensed data to the platform after performing the task; the platform generally uses the CRH method to calculate the true value, and sends the result of true value calculation to the DR, thereby completing the whole data collection process.

[0004] However, the above general task collection method of mobile crowdsensing network has the following disadvantages: first, there is no privacy protection, and the information of the worker is directly exposed. The worker is worried about his privacy and does not want to participate in data collection, which will hinder the development of crowdsensing network. The second disadvantage is that the CRH method commonly used in the current data collection method recruits N workers to execute a task to obtain N data, and then calculates the true value by weighting the N data. The basic idea of true value calculation is that the data submitted by the worker is closer to the true value, and vice versa. The CRH method is as follows: most of the workers in the network are trustworthy, so the data submitted by them is true, and thus is close to the true value calculated finally, so the weight of the trustworthy worker is large, while the weight of the untrustworthy worker or malicious worker is small, and the result of the weighted calculation is close to the actual true value. However, the data quality obtained by this method is not necessarily good. In this method, as long as there is false data in the N data, the data quality will be reduced, and the more false data, the lower the quality. When multiple malicious workers join forces to attack, the attack can get the result they want, so the CRH method is completely ineffective.

[0005] In order to change this situation, some researchers have proposed a worker recruitment method based on reputation selection. The idea of the method is: the reputation evaluation of the worker; then, select the worker with high reputation to obtain high-quality data. However, the challenge of this method is that the reputation of the worker is the private information of the worker, and the worker is unwilling to let the platform know the reputation information of the worker. Therefore, it is a great challenge to collect data under the condition of privacy protection of trust, and there is no good solution at present.

[0006] The prior art does not meet the high data quality and worker reputation privacy protection, therefore, an invention method is needed to solve the above problems. SUMMARY

[0007] Therefore, the present application provides a trusted worker recruitment method based on identity privacy protection to solve the problems in the background art.

[0008] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0009] A trusted worker recruitment method based on identity privacy protection, characterized in that it comprises the following steps:

[0010] The data requester submits a task request to the cloud server;

[0011] The cloud server publishes the task to the worker;

[0012] The worker who wants to participate in the task registers his identity with the trust authentication center, and generates an elliptic curve encryption key pair locally when registering; After retaining the private key, the public key is sent to the authentication center, and the authentication center will generate a pseudonym for the worker according to the public key of the worker;

[0013] The authentication center sends the pseudonym to the corresponding worker;

[0014] The worker applies for a sensing task to the cloud server with his own pseudonym and reputation value, and the reputation value of the worker is obtained by querying the authentication center based on his own identity;

[0015] The cloud server interacts with the authentication center to verify whether the reputation value of the worker is tampered with, and only the worker who passes the verification will be recruited;

[0016] The cloud server selects N workers from the workers who pass the verification to perform the sensing task, and notifies the selected workers;

[0017] The selected workers sense the data and submit the data to the cloud server;

[0018] The cloud server performs true value calculation and reputation evaluation on the sensing data submitted by the worker;

[0019] The cloud server sends the true value to the data requester and sends the reputation value evaluation result to the authentication center.

[0020] The authentication center updates the reputation value of the worker.

[0021] Optionally, the pseudonym refers to replacing the personal identity information with a pseudo identifier to ensure that the data subject cannot be identified through the data without using other additional information, which is an identity privacy protection technology; the worker generates an elliptic curve encryption key pair locally, wherein the public key is Pk i , and the private key is Sk i ; the worker sends the public key Pk i to the authentication center during registration; using SHA1 algorithm as a hash function, denoted as Hash(·), a unique salt value salt i is added to each worker ω i before the hash algorithm processing, the salt value is a random string salt i ← Rand(l) of any length l, the salt value is spliced to the worker public key Pk i as an attribute, and then the hash processing is performed to calculate the pseudonym identifier p i = Hash(Pk i || salt i ), wherein “||” represents a string splicing operation; finally, the worker uses the pseudonym p i to apply to the cloud server for participating in the perception task; since the pseudonym of each worker is generated by a unique salt value, and the cloud server cannot access the original Pk i and salt i , it is prevented that the cloud server or the data requester links to the specific user identity through the perception data.

[0022] Optionally, a two-party reputation verification process based on zero-knowledge proof is adopted, so that the cloud server proves to the authentication center whether the reputation submitted by the worker is true without obtaining the true value of the worker's reputation;

[0023] The zero-knowledge proof is used to verify whether the reputation value of the worker is equal to the reputation value stored in the authentication center, i.e., whether the reputation value of the worker is tampered with;

[0024] The prover proves to the verifier that the reputation value t' possessed by the prover is the same as the reputation value t possessed by the verifier; at the same time, the prover will not disclose t' to the verifier in this process; according to the reputation value, the verifier measures whether the reputation value of each prover passes the verification through the formula υ = ν(t, t').

[0025] Optionally, the trusted center acts as a verifier, while the worker is a prover, the worker proves to the server that the reputation value it possesses is valid, and promises to prevent the cloud server from knowing the reputation value, thereby protecting the privacy of the worker's reputation value, the specific process is as follows:

[0026] G is a cyclic group with prime order p, g1 is an element of , g2, h1 and h2 are elements generated by g1, based on the discrete logarithm based on h1, the discrete logarithm based on g1, the discrete logarithm based on h2, the discrete logarithm based on g2, the worker cannot know; assume that t is the reputation value of the worker stored by the trusted center, t' is the reputation value submitted by the worker; l, s1, s2 are three security parameters;

[0027] The worker who is willing to participate in the perception task first selects three random integers ω, λ1, λ2, which satisfy ω∈[1,2 l ·p+1], Then calculate Through And Get Then, the worker calculates D=ω+H·t′,D1=λ1+H·r1 and D2=λ2+H·r2; Hash(·) is a hash function; finally, the worker sends H, D, D1, D2, C m ′ to the cloud server;

[0028] After receiving the data from the worker side, the cloud server obtains the commitment From the trusted center, where t is the reputation value of the worker stored by the trusted center and

[0029] The cloud server verifies whether the reputation value is tampered with by checking whether H and are equal; if they are equal, the reputation value of the worker is not tampered with, and the worker passes the verification; otherwise, the verification fails, and the worker will not be recruited by the server to participate in the subsequent steps; in order to facilitate the description below, if u k passes the trust verification algorithm, then υ k =ν(t,t′)=1; otherwise, υ k =ν(t,t′)=0; only the worker who passes the trust verification will be recruited to participate in the subsequent true value calculation.

[0030] Optionally, a CRH-based true value discovery and reputation evaluation method is adopted, which optimizes the data source weight and task true value through iteration, thereby improving the data quality;

[0031] The true value calculation process is a process of continuously iteratively calculating the estimated true value of the data; at the beginning of the iteration, the average value of the data collected for the current task is first calculated. Where Q represents the total number of workers who submitted this task data; then the standard deviation of the current task data is calculated based on this average value Calculate the data submitted by a single worker and the estimated true value x of the data through standard deviation * The standard error between At the beginning of the iteration, set To obtain the standard error of the task data submitted by each worker; then based on the data submitted by the worker and the estimated true value x * The standard error between them is used to calculate the data weight of the data submitted by the workers. Bring the worker's data weight into x * The calculation formula Thus, a new round of estimated true value is obtained; this iterative process is repeated until the estimated true value x * The iteration ends after the value of converges, and then the data weights of the workers at the time of convergence are observed. The k workers with higher scores are selected, and their performance in this round is marked as credible. Their data is adopted and rewards are issued. The cloud server sends the data weights of the workers to the authentication center. The authentication center divides the workers into three categories according to their reputation values: malicious, unknown, and credible. If the reputation value of the worker is greater than the high threshold T h , then it belongs to the credible set; if the reputation value of the worker is in T h With T l If the reputation value is less than the lower threshold T l , then it belongs to the malicious set; the authentication center selects the trusted workers among them, assuming that there are K trusted workers in total, and calculates the average of their data weights Obtaining task scores for unknown workers If the worker's task score ρ i Greater than or equal to the task score threshold ρ △ , that is, ρ i ≥ρ △ , then through t new =min{(1-μ)ρ+ηt,1} to calculate the new reputation value after the increase; otherwise, if ρ i <ρ △ , then through t new =max{-(1-μ)(1-ρ i )+ηt,0} to calculate the new reduced reputation value, where η is called the rate coefficient, which is used to control the degree of influence of the historical reputation value on the new reputation value; after the reputation value of a worker is updated, the certification center classifies it into the corresponding category based on the updated reputation value.

[0032] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention provides a trusted worker recruitment method based on identity privacy protection, which performs well in terms of data quality and achieves the lowest mean absolute error and the lowest root mean square error in the experiment, with reductions of 21.75% and 80.17%, respectively. It also has good performance in worker reputation identification and has the function of privacy protection of worker reputation. In summary, the present invention achieves privacy protection, improves data quality, and provides an efficient, reliable and innovative solution for the widespread application of group intelligence networks, which has important practical application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0034] Figure 1 A diagram of the system network structure of the invention method;

[0035] Figure 2 Obtain the mean absolute error and root mean square error of the data;

[0036] Figure 3 Changes in the credibility of different workers. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] Example 1

[0039] An embodiment of the present invention discloses a trusted worker recruitment method based on identity privacy protection, comprising the following steps:

[0040] Step 1: The data requester (DR) submits a task request to the cloud server (CS).

[0041] Step 2: CS issues the task to the worker;

[0042] Step three, the worker who wants to participate in the task registers his identity to the Trust Authority (TA) and generates an elliptic curve encryption key pair locally. The worker sends the public key to the TA and the TA generates a pseudonym for the worker according to the public key. The specific process is described in clause 2.

[0043] Step four, the TA sends the pseudonym to the corresponding worker.

[0044] Step five, the worker applies for a sensing task to the CS using his pseudonym and reputation value. The worker's reputation value can be obtained by querying the TA with his identity.

[0045] Step six, the CS interacts with the TA to verify whether the worker's reputation value has been tampered with. Only workers who pass the verification will be recruited. The specific process of reputation verification is described in clause 3.

[0046] Step seven, the TA returns the reputation value verification result to the CS.

[0047] Step eight, the CS selects N workers from the workers who pass the verification to perform the sensing task and notifies the selected workers.

[0048] Step nine, the selected workers sense the data and submit the data to the CS.

[0049] Step ten, the CS performs true value calculation and reputation evaluation on the sensing data submitted by the workers. The specific process of true value discovery and reputation evaluation is described in clause 4.

[0050] Step eleven, the CS sends the true value to the DR and the reputation value evaluation result to the TA. The reputation value can only change in two ways, up or down.

[0051] Step twelve, the TA updates the worker's reputation value.

[0052] Further, the present embodiment uses a salted hash algorithm to realize pseudonymization to improve the unpredictability of traditional hash pseudonymization, prevent rainbow table and repeated password attacks, and thus enhance the degree of privacy protection for worker identity.

[0053] Pseudonymization refers to replacing personal identity information with a pseudo identifier to ensure that without using other additional information, the data subject cannot be identified through the data. The worker generates an elliptic curve encryption key pair locally, where the public key is Pk i and the private key is Sk i . The worker sends the public key Pk i to the TA. The present invention uses the SHA1 algorithm as a hash function (denoted as Hash(·)). Before the hash algorithm is processed, a salt value is first generated for each worker ω iAdd a unique salt value salt i , the salt value is a randomly generated string of any length l i ←Rand(l), concatenate the salt value as an attribute to the worker's public key Pk i After that, hash processing is performed to calculate the worker's pseudonym identification p i =Hash(Pk i ||salt i )

[0054] , where “||” represents the string concatenation operation. Finally, the worker uses the pseudonym p i Apply to the cloud server to participate in the perception task. Since each worker's pseudonym is generated by its unique salt value and CS cannot access the original Pk i and salt i , thus preventing CS or DR from linking to a specific user identity through sensing data.

[0055] Furthermore, this embodiment adopts a two-party reputation verification process based on zero-knowledge proof, which allows the CS to verify the authenticity of the reputation submitted by the worker from the TA without obtaining the true value of the worker's reputation;

[0056] Zero-knowledge proof is a cryptographic verification mechanism that allows a prover to prove to a verifier that they know a message m using a cryptographic commitment scheme, without revealing any other information about m. In this paper, we use zero-knowledge proof to verify whether a worker's reputation value is equal to the reputation value stored in the TA, that is, whether the worker's reputation value has been tampered with.

[0057] The prover proves to the verifier that its reputation value t′ is the same as the verifier's reputation value t. The prover does not reveal t′ to the verifier during this process. Based on the reputation value, the verifier uses the formula υ = ν(t, t′) to measure whether each prover's reputation value passes verification.

[0058] In this method, the trusted center acts as the verifier, while the worker acts as the prover. The worker must prove to the server that their reputation value is valid. Pedersen commitments prevent the cloud server from knowing the reputation value, thus protecting the privacy of the worker's reputation value. The specific process is as follows.

[0059] Step 1. G is a cyclic group with prime order p, g1 is The elements of g2, h1, and h2 are generated by g1, and the discrete logarithm based on h1, the discrete logarithm based on g1, the discrete logarithm based on h2, and the discrete logarithm based on g2 are unknown to the worker. Assume that t is the reputation value of the worker stored by the trusted center, and t′ is the reputation value submitted by the worker (which may be tampered with). l, s1, and s2 are three security parameters.

[0060] Step 2. The workers who are willing to participate in the sensing task first select three random integers ω, λ1, λ2, which satisfy ω∈[1,2 l ·p+1], Then calculate pass and get Then, the worker calculates '

[0061] D=ω+H·t, D1=λ1+H·r1 and D2=λ2+H·r2. Hash(·) is a hash function. Finally, the worker will H,D,D1,D2,C m ’ is sent to the cloud server.

[0062] Step 3. After receiving data from the worker, the cloud server obtains the commitment from the trusted center where t is the worker reputation value stored by the trusted center and

[0063] Step 4. The cloud server checks H and To verify whether the reputation value has been tampered with, we need to check whether they are equal. If they are equal, the reputation value of the worker has not been tampered with and the worker passes the verification. Otherwise, the verification fails and the worker will not be recruited by the server to participate in the subsequent steps. For the convenience of the following description, if u k Through the trust verification algorithm, υ k =ν(t,t′)=1; otherwise, υ k =ν(t,t′)=0. Only workers who pass the trust verification will be recruited to participate in the subsequent truth value calculation.

[0064] Furthermore, this embodiment adopts a CRH-based truth discovery and reputation evaluation method to improve data quality by iteratively optimizing data source weights and task truth values;

[0065] The true value calculation process of the present invention is a process of continuously iteratively calculating the estimated true value of the data. At the beginning of the iteration, the average value of the data collected by the current task is first calculated. Where Q represents the total number of workers who submitted this task data; then the standard deviation of the current task data is calculated based on this average value Through the standard deviation, we can calculate the data submitted by a single worker and the estimated true value x of the data * The standard error between At the beginning of the iteration, we set To obtain the standard error of the task data submitted by each worker; then based on the data submitted by the worker and the estimated true value x * We can calculate the data weight of the data submitted by the workers by the standard error between Then we bring the worker's data weight into x * The calculation formula Thus we get a new round of estimated true value. We repeat this iterative process until the estimated true value x * The iteration ends after the value of converges. Then, the data weights of the workers at the time of convergence are observed, and the k workers with the highest performance are selected. Their performance in this round is marked as trustworthy, and their data is adopted and rewards are issued. The CS will send the data weights of these workers to the TA. The TA will classify the workers into three categories: malicious, unknown, and trustworthy according to their reputation value. If the reputation value of the worker is greater than the high threshold, It belongs to the trusted set; if the reputation value of the worker is and If the reputation value is less than the lower threshold, it belongs to the unknown set. It belongs to the malicious set; TA selects the trusted workers (assuming there are K in total) and calculates the mean of their data weights Obtaining task scores for unknown workers If the worker's task score ρ i Greater than or equal to the task score threshold ρ △ , that is, ρ i ≥ρ △ , then through t new =min{(1-μ)ρ+ηt,1} to calculate the new reputation value after the increase; otherwise, if ρ i <ρ △ , then through t new =max{-(1-μ)(1-ρ i )+ηt,0} to calculate the new, reduced reputation value. η in both formulas is called the rate coefficient, which controls the degree to which the historical reputation value affects the new reputation value. After a worker's reputation value is updated, the TA assigns the worker to the corresponding category based on the updated reputation value.

[0066] Example 2

[0067] The difference between this embodiment and embodiment 1 is that:

[0068] In the crowd sensing network, real data of a certain place needs to be obtained, and high data quality is required for sensing application scenarios such as temperature and humidity, air quality, traffic conditions and the like, and the sensed data is uploaded to the platform for processing. In the task publishing of the platform, workers apply for participating in the task with pseudonyms, and different data provided by the workers. The method of the application can improve the accuracy of the data without revealing the credibility of the workers in the above-mentioned data collection application.

[0069] The experimental results of the method of the application are given below.

[0070] Figure 1 The network structure diagram of the method of the application is given. The method of the application has the following components: a reputation authentication center, which is trusted; a server, which is curious and cautious, and will strictly perform the data collection process, but may be curious to obtain the credibility of the workers; a data requester, which is an entity that needs data; a large number of workers, which can execute sensing tasks. The specific data collection process is described in the description of the claims of the method of the application.

[0071] Figure 2 The experimental results of the method of the application and other methods in data accuracy are given. The methods compared with the method of the application are two: one is the CRH method, that is, N workers are recruited to execute a sensing task at the same time, and then the average value of the N data obtained is taken as the final result; the other is the CRH method with privacy protection, which does not select workers according to the credibility of the workers; and finally the method of the application. From the experimental results Figure 2 , and the corresponding experimental data Table 1 and Table 2, it can be seen that since the credibility of the workers is verified and the true value is calculated according to the credibility of the workers in the method of the application, the result obtained is more accurate. Compared with the other two methods, the average absolute error of the method of the application decreases by 21.75% after a long period of data collection. The root mean square error decreases by 80.17%.

[0072] Table 1: Comparison of different methods in average absolute error

[0073]

[0074]

[0075] Table 2: Comparison of different methods in root mean square error

[0076]

[0077] Figure 3The change of the credibility of the worker in the method is given. As can be seen from the experimental result graph, the credibility of the credible worker is continuously rising, and the credibility of the worker without integrity is continuously declining, which shows that the method can effectively identify the credibility of the worker, thereby guiding to select the credible worker to collect data and improve the data quality.

[0078] The various embodiments are described in the specification with progressive progression from simple to complex, each embodiment highlighting a different aspect of the application over previous embodiments. The same reference numerals in different embodiments designate the same or similar elements. For the embodiments of the device, the description is relatively simple as it corresponds to the embodiments of the method, and the relevant parts are referred to the description of the method.

[0079] The above description of disclosed embodiments enables a person skilled in the art to implement or use the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A trusted worker recruitment method based on identity privacy protection, characterized in that: The following steps are involved: The data requester submits a task request to the cloud server; The cloud server publishes tasks to workers; Workers who wish to participate in the task register their identities with the trusted authentication center. During registration, the worker generates an elliptic curve encryption key pair locally. After retaining the private key, the public key is sent to the certification center, which will generate a pseudonym for the worker based on the worker's public key; The certification center sends the pseudonym to the corresponding worker; Workers use their pseudonyms and reputation values ​​to apply for sensing tasks from the cloud server. The reputation values ​​of workers are obtained by querying the authentication center based on their identities. The cloud server interacts with the certification center to verify whether the worker's reputation value has been tampered with. Only workers who pass the verification will be recruited; The cloud server selects N workers from the verified workers to perform the sensing task and notifies the selected workers; The selected workers perceive the data and submit it to the cloud server; The cloud server performs truth value calculation and reputation evaluation on the perception data submitted by workers; The cloud server sends the true value to the data requester and the reputation value evaluation result to the authentication center; The certification center updates the reputation value of workers.

2. A trusted worker recruitment method based on identity privacy protection according to claim 1, characterized in that: Pseudonymization refers to an identity privacy protection technology that replaces personal identity information with a pseudo-identifier to ensure that the data subject cannot be identified through the data without using other additional information; the worker generates an elliptic curve encryption key pair locally, where the public key is Pk i , the private key is Sk i When registering, workers will provide their public key Pk i Send to the authentication center; use SHA1 algorithm as the hash function, recorded as Hash(·), before the hash algorithm is processed, first for each worker ω i Add a unique salt value salt i The salt value is a randomly generated string of any length l. i ←Rand(l), concatenate the salt value as an attribute to the worker’s public key Pk i After that, hash processing is performed to calculate the worker's pseudonym identification p i =Hash(Pk i ||salt i ), where "||" represents a string concatenation operation; finally, the worker uses the pseudonym p i Apply to the cloud server to participate in the perception task; since each worker's pseudonym is generated by its unique salt value, and the cloud server cannot access the original Pk i and salt i , thus preventing the cloud server or data requester from linking to the specific user identity through the perception data.

3. The trusted worker recruitment method based on identity privacy protection according to claim 1 is characterized in that: A two-party reputation verification process based on zero-knowledge proof is adopted, which allows the cloud server to verify the authenticity of the reputation submitted by the worker with the certification center without obtaining the true value of the worker's reputation; Use zero-knowledge proof to verify whether the worker's reputation value is equal to the reputation value stored in the certification center, that is, whether the worker's reputation value has been tampered with; The prover proves to the verifier that the reputation value t′ owned by the prover is the same as the reputation value t owned by the verifier; at the same time, the prover will not reveal t′ to the verifier during this process; based on the reputation value, the verifier measures whether the reputation value of each prover has passed the verification through the formula υ=ν(t,t′).

4. The trusted worker recruitment method based on identity privacy protection according to claim 1, characterized in that: The trusted center acts as a verifier, while the worker is the prover. The worker proves to the server that the reputation value they have is valid. The promise is used to prevent the cloud server from knowing the reputation value, thereby protecting the privacy of the worker's reputation value. The specific process is as follows: G is a cyclic group of prime order p, g1 is The elements of g2, h1 and h2 are elements generated by g1, the discrete logarithm based on h1, the discrete logarithm based on g1, the discrete logarithm based on h2, and the discrete logarithm based on g2, which are unknown to the workers. Assume that t is the reputation value of the worker stored in the trusted center and t′ is the reputation value submitted by the worker. l, s1, s2 are three security parameters. Workers who are willing to participate in the sensing task first select three random integers ω, λ1, λ2, which satisfy ω∈1,2 l ·p+1], Then calculate pass and get Then, the worker calculates D=ω+H·t′, D1=λ1+H·r1 and Hash(·) is a hash function; finally, the worker converts H,D,D1,D2,C m 'Send to the cloud server; After receiving data from the worker, the cloud server obtains the commitment from the trusted center where t is the worker reputation value stored by the trusted center and The cloud server checks H and Are they equal to verify whether the reputation value has been tampered with; if they are equal, the reputation value of the worker has not been tampered with and the worker passes the verification; otherwise, the verification fails and the worker will not be recruited by the server to participate in the subsequent steps; for the convenience of the following description, if u k Through the trust verification algorithm, υ k =ν(t,t′)=1; otherwise, υ k =ν(t,t′)=0; only workers who pass the trust verification will be recruited to participate in the subsequent truth value calculation.

5. The trusted worker recruitment method based on identity privacy protection according to claim 1 is characterized in that: A CRH-based truth discovery and reputation evaluation method is adopted to improve data quality by iteratively optimizing data source weights and task truth values. The true value calculation process is a process of continuously iteratively calculating the estimated true value of the data; At the beginning of the iteration, the average value of the data collected by the current task is calculated first. Where Q represents the total number of workers who submitted this task data; then the standard deviation of the current task data is calculated based on this average value Calculate the data submitted by a single worker and the estimated true value x of the data through standard deviation * The standard error between At the beginning of the iteration, set To obtain the standard error of the task data submitted by each worker; then based on the data submitted by the worker and the estimated true value x * The standard error between them is used to calculate the data weight of the data submitted by the workers. Bring the worker's data weight into x * The calculation formula Thus, a new round of estimated true value is obtained; Repeat this iterative process until the estimated true value x * The iteration ends after the value converges, and then the data weights of the workers at the time of convergence are observed. The k workers with the highest performance are selected and marked as trustworthy in this round. Their data is adopted and rewards are issued. The cloud server sends the data weights of the workers to the authentication center. The authentication center divides the workers into three categories: malicious, unknown and trustworthy according to their reputation values. If the reputation value of the worker is greater than the high threshold, It belongs to the trusted set; if the reputation value of the worker is and If the reputation value is less than the lower threshold, it belongs to the unknown set. It belongs to the malicious set; the authentication center selects the trusted workers among them, assuming there are K trusted workers in total, and calculates the average of their data weights Obtaining task scores for unknown workers If the worker's task score ρ i Greater than or equal to the task score threshold ρ △ , that is, ρ i ≥ρ △ , then through t new =min{(1-μ)ρ+ηt,1} to calculate the new reputation value after the increase; otherwise, if ρ i <ρ △ , then through t new =max{-(1-μ)(1-ρ i )+ηt,0} to calculate the new reduced reputation value, where η is called the rate coefficient, which is used to control the degree of influence of the historical reputation value on the new reputation value; after the reputation value of a worker is updated, the certification center classifies it into the corresponding category based on the updated reputation value.

Images