A low-cost high-quality data acquisition method with credibility and privacy protection

Abstract

The application discloses a low-cost high-quality data acquisition method with credibility and privacy protection, and relates to the fields of credibility calculation, privacy protection and low-cost data collection of a crowd intelligence network. In the application, the bid of a worker and the credibility are sent to a blockchain after homomorphic encryption, the ratio of the credibility to the bid is realized through a homomorphic encryption and re-encryption method under privacy protection, and the worker with high credibility and low bid is selected according to the ratio. Then, the true value is obtained through privacy calculation based on trust true value discovery between a server and a data requester, and the trust evaluation is obtained, so that the privacy, high-quality and low-cost data acquisition is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of trusted computing, privacy protection and low-cost data collection of crowd intelligence network, and more particularly to a trusted and privacy-protected low-cost high-quality data acquisition method. 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 sensing tasks, and has shown great application potential in many fields such as military, industry, health and environment monitoring. However, the wide application of crowdsensing technology is faced with the problems of privacy protection and inaccurate data. The main task of crowdsensing technology is to recruit a large number of people carrying sensing devices, called workers, to collect data. Since the workers are widely distributed and numerous, crowdsensing technology can achieve large-scale and timely data acquisition. In crowdsensing technology, obtaining high-quality data is an important research topic. Only high-quality data can construct high-quality applications, while low-quality data will construct low-quality applications. However, there are challenges in obtaining high-quality data. The main reason is that in the crowdsensing network, workers need to pay for sensing data and submitting data. The platform needs to give workers certain rewards to encourage workers to participate in data collection. The data submitted by the workers is difficult to verify the quality, that is, after the platform receives the data, it still does not know the quality. In this case, malicious workers and dishonest workers will report false data to cheat rewards.

[0003] On the other hand, the requirement of privacy protection makes the data collection more challenging. The main goal of data privacy protection is to protect the data content of workers without letting others know. Because, the data of workers, the price and other information are sensitive or private. If workers are worried about privacy issues, they will not participate in data collection, which will hinder the development of crowd sensing. A simple way of privacy protection is that the data requester publishes the public key of encrypted data at the same time when publishing the task through the platform, and the worker uploads the collected data to the platform after encrypting it with the public key, then the platform transfers the data to the data requester. The data requester decrypts the data with the private key to get the plaintext data. However, the privacy protection of this method is weak, because in this method, the data requester actually knows the data of the worker, and at this time the data of the worker is leaked to the data requester. Therefore, although the encryption method is used, the platform cannot know the data of the worker, but the data requester still knows the data of the worker. Therefore, in order to protect the data privacy of workers, there are still many privacy protection researches afterwards. But these privacy protection researches still have the following shortcomings. One is that the current privacy protection rarely considers data quality, which will lead to the protection of data quality. The reason for this situation is: privacy protection hides the information of workers, and to obtain high-quality data, it is necessary to evaluate the quality of the data submitted by the workers, and select high-quality workers to collect data after evaluation, so as to obtain high-quality data. In privacy protection, the data of workers cannot be obtained by the platform, so it is difficult to evaluate the quality, and it is difficult to select workers according to the results of quality evaluation, so that the high-quality acquisition method in privacy protection method is challenged. Moreover, in data acquisition, it is also hoped to reduce the cost as much as possible, however, reducing the cost needs to select high-quality and low-priced workers from workers, on the one hand, it is difficult to design a method of selecting high-quality and low-priced workers; and more difficult is that in privacy protection, the price of workers is also private information, which cannot be obtained by any third party. Under such privacy protection, it is very difficult to obtain high-quality and low-priced workers. Therefore, in view of the deficiencies of the prior art in simultaneously meeting high data quality, low price and privacy protection, there is an urgent need for an invention method that can solve the above problems comprehensively. SUMMARY

[0004] In view of this, the present application provides a trusted and privacy-protected low-cost high-quality data acquisition method. Compared with the previous strategy, the present application mainly proposes an effective method for identifying trusted workers under the premise of guaranteeing the privacy protection of data content and price, so as to select trusted workers to collect data, thereby effectively improving the data quality and realizing the privacy protection of data content and price. The target of the method of the present application is threefold. First, the privacy of the data collected by the workers and the privacy of the price are protected, so that they cannot be known by any third party except the workers themselves. Second, trusted workers are selected so that the quality of the collected data is high, and the trustworthiness of the workers cannot be known by any irrelevant third party. Third, workers with low prices are selected to reduce costs. In the present application, the price and trustworthiness of the workers are sent to the blockchain after homomorphic encryption, and the ratio of trustworthiness to price is realized through homomorphic encryption and re-encryption method under the condition of privacy protection. Then, the workers with high trustworthiness and low price are selected according to the ratio. Then, the true value is obtained through the privacy calculation based on trust true value discovery between the server and the data requester, and the trust evaluation is realized, thereby realizing the privacy-protected, high-quality and low-cost data acquisition.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] A trusted and privacy-protected low-cost high-quality data acquisition method comprises the following steps:

[0007] The data requester publishes a task on the data transaction center;

[0008] The worker applies to perform the task according to his own will, and encrypts his own price for doing the task using his own public key and uploads it to the blockchain of the data transaction center;

[0009] The data requester removes the workers with trustworthiness less than a preset threshold from the worker set applying for the task, and encrypts the trustworthiness of the remaining workers using the public key of the data requester to generate ciphertext and sends it to the data transaction center;

[0010] The data requester generates a re-encryption key with the public key of the server and his own and sends it to the server. The server encrypts the data encrypted by its own public key using the re-encryption key, and then decrypts it using its own private key to obtain the ratio of the trustworthiness of the worker to the price;

[0011] The workers are sorted according to the ratio of the trustworthiness to the price, and the sorted result is given to the data requester. The data requester selects the workers according to the trust status of the workers, and selects the workers from high to low according to the ratio The worker is notified to perform the task, and the worker performs the task.

[0012] Optionally, for each worker , a cyclic group is generated using a generator . , the order is ; workers select one from the set , and then calculate ; each worker generates a public key and publishes it, and its private key , which is not disclosed, and the public key of worker is denoted by ; the public keys of the data requester and the server are denoted by and respectively, and the private keys of the data requester and the server are denoted by , respectively;

[0013] The set of workers is denoted by , the set of trusted workers is denoted by , and the initial trust degree of the workers in the set is 1; the trust degrees of the remaining other workers are unknown, and the set is denoted by , and the trust degree is 0.5; the trust degree of a worker is between [0, 1], 0 represents low trust degree, and 1 represents the highest trust degree; the set of malicious workers is denoted by , and is initially empty; the trust degree of worker is .

[0014] Optionally, the worker encrypts his own bid for the task with his own public key and uploads it to the blockchain, and the expression is as follows:

[0015] (1)

[0016] denotes the public key of worker , denotes the ciphertext after encrypting the bid , and denotes the encryption of with the key .

[0017] Optionally, the data requester removes the workers in the set of workers applying for the task whose trust degree is less than a preset threshold, encrypts the trust degrees of the remaining workers with the public key to generate ciphertext and sends it to the data transaction center, and the expression is as follows: remove the workers in the set of workers applying for the task whose trust degree is less than the threshold , encrypt the trust degrees of the remaining workers with the public key to generate ciphertext and send it to the data transaction center;

[0018] (2).

[0019] Optionally, the server generates a random number for each worker , and encrypts it with the worker's public key and sends it to the data exchange center;

[0020] (3).

[0021] Optionally, the smart contract on the blockchain uses homomorphic encryption to multiply the constant by the performance calculation:

[0022] (4).

[0023] Optionally, after the worker completes the task, the server interacts with the data requester to obtain the weight, the specific steps are as follows:

[0024] The server selects a data as ;

[0025] Save the true value of the previous round = , calculate the distance between each worker and ;

[0026] = (12)

[0027] The server sends the difference value of each worker to the data requester, and the data requester receives difference values of workers, calculates the real distance between and ;

[0028] = (12)

[0029] Calculate the weight of each worker according to the following formula:

[0030] (13)

[0031] The data requester sends of each worker to the server;

[0032] The server lets , calculates according to the following formula:

[0033] (14)

[0034] If the value of is greater than the threshold Then return to calculating each worker and The distance, the cycle, continues.

[0035] Optionally, after the worker completes the task, the data requester updates the worker's trust level, following these steps:

[0036] The data requester will be aware of the task. The workers belong to the set The weights of trusted workers are averaged to determine the perception task. The workers belong to the set The set of trustworthy workers is The number of sets is ;

[0037] | refers to a set Workers in the middle (15)

[0038] The computation is not in the set The weight of each worker in the middle and difference rate As shown in the following formula:

[0039] (16)

[0040] if Less than the threshold Then, increase workers' trust level according to formula (17). Conversely, worker trust levels decrease according to formula (18). ;

[0041] (17)

[0042] (18)

[0043] If the updated workers Greater than the upper threshold ,Worker Included in the set of trustworthy workers If the updated workers Less than the lower threshold ,Worker Included in the malicious worker set ;

[0044] Workers are re-weighted based on their level of trust: if workers... Classified as a malicious worker So, the workers weight , other workers, their weights are their trust degrees. Therefore, the weight update is calculated as follows:

[0045] (19).

[0046] Optionally, the server calculates , the server sends to the data requester, and the data requester obtains as the final true value:

[0047] .

[0048] Via the technical solution described above, compared with the prior art, the present application provides a method aiming at privacy protection of workers' data and bid price unknown to any owner, and under the premise of privacy protection, high-quality, low-bid workers can be selected to obtain high-quality data. Experimental results show that the method of the present application can realize privacy protection of data content and worker bid price, and high-quality, low-bid workers can be selected to obtain accurate data. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only a part of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0050] Figure 1 is a structural schematic diagram of the present application;

[0051] Figure 2 obtaining the average absolute error of data;

[0052] Figure 3 the trust degree change of different types of workers with data collection. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Embodiment 1

[0055] The embodiment of the application discloses a trusted and privacy-protected low-cost high-quality data acquisition method, comprising the following steps:

[0056] The data requester publishes a task on the data transaction center;

[0057] The worker applies to perform the task according to his own will, and encrypts his own bid for the task using his public key and uploads it to the blockchain of the data transaction center;

[0058] The data requester removes the workers with a trust degree less than a preset threshold from the worker set applying for the task, and sends the ciphertext generated by encrypting the trust degrees of the remaining workers using the public key of the data requester to the data transaction center;

[0059] The data requester generates a re-encryption key with the public key of the server and his own, and sends the re-encryption key to the server, and the server encrypts the data encrypted by the public key of the server using the re-encryption key, and then decrypts the data using the private key of the server to obtain the ratio of the trust degree of the worker to the bid;

[0060] According to the ratio of the trust degree of the worker to the bid, the sorted result is given to the data requester, and the data requester selects the worker according to the trust state of the worker, and selects the worker from high to low according to the ratio The data requester selects the worker according to the trust state of the worker, and selects the worker from high to low according to the ratio

[0061] Among them, the ElGamal cipher system is a public key encryption algorithm based on the discrete logarithm problem, and the basic definition and operation process are as follows:

[0062] (1) Key generation: in a cyclic group with order , the generator is used. A is randomly selected, and is calculated. Finally, the public key is represented as , and the private key is .

[0063] (2) Encryption process: the message is encrypted using the public key and the private key. The encryption process is as follows:

[0064]

[0065] (3) Decryption process: in order to decrypt , the study first calculates , and according to the key , the decryption formula is as follows:

[0066]

[0067] (4) Homomorphic property: the ElGamal cryptosystem has a homomorphic property, that is, the encrypted ciphertext can be operated homomorphically.

[0068] (c)

[0069]

[0070]

[0071] Initialization: for each worker , a cyclic group is generated with a generator , and the order of the group is . Worker selects a from the set , and then calculates ; each worker generates a public key and publishes it, and the private key is kept secret. In the present application, the public key of worker is denoted by ; similarly, the data requester and the server also generate their own public and private keys, which are denoted by and , respectively, and , represent the public keys of the data requester and the server, respectively, and , represent the private keys of the data requester and the server, respectively.

[0072] The set of workers is denoted by , and the data requester initially knows that a certain number of workers are trustworthy, and the set is denoted by , and the initial trust degree of the workers in the set is 1; the trust degree of the remaining other workers is unknown, and the set is denoted by , and the trust degree is 0.5; the trust degree of the worker is between [0, 1], 0 represents low trust degree, and 1 represents the highest trust degree; the set of malicious workers is denoted by , and is initially empty; the trust degree of worker is denoted by .

[0073] Specifically, the present application provides a trusted and privacy-protected low-cost high-quality data acquisition method, which comprises the following steps:

[0074] (1) The data requester publishes a task on the data transaction center, i.e., the data transaction center;

[0075] (2) The worker checks the task;

[0076] (3) If the worker is willing to perform the task, the worker does the following two actions: the worker asks for the task from the data requester; the worker encrypts his own bid for the task with his public key and uploads it to the blockchain;

[0077] (1)

[0078] represents the public key of the worker , represents the encrypted ciphertext of the bid , represents that the ciphertext is encrypted with the key , and the same applies hereafter;

[0079] (4) First, a selection is made to remove the workers in the set of workers who applied for the task whose trustworthiness is less than a threshold value , and then the trustworthiness of the remaining workers is encrypted with the public key to generate ciphertext and sent ;

[0080] (2)

[0081] (5) The server generates a random number for each worker and encrypts it with the public key of the worker and sends it to the data transaction center;

[0082] (3)

[0083] (6) The smart contract on the blockchain uses homomorphic encryption to calculate the performance of multiplying a constant by

[0084] (4)

[0085] At this point , there are , , and in , , and are not meant to represent only one worker , but rather a set, with the th worker representing it;

[0086] (7)​ are encrypted with a public key of the data requester . Thus, the two encrypted numbers cannot be homomorphically computed; thus, this step converts to data encrypted with the same public key; the worker first uses the public key of the data requester and his own to generate a re-encryption key and sends it to the data transaction center;

[0087] (8) The data transaction center re-encrypts with the public key of the data requester and sends the encrypted ciphertext, although now encrypted with the public key of the data requester, the data requester does not know the random number and thus cannot know ;

[0088] (5)

[0089] (9) The data transaction center re-encrypts and and performs the following operation in an encrypted state;

[0090] (6)

[0091] (10) The data transaction center sends to the server, which only knows even if it can decrypt it, it cannot know and ;

[0092] (11) The server computes

[0093] (7)

[0094] (12) The data requester uses the public key of the server and his own to generate a re-encryption key and sends it to the server, which uses the re-encryption key to encrypt data encrypted with its own public key, and then uses its private key to decrypt as in equation 9 to obtain the ratio of the worker's trustworthiness to the bid, and the server still cannot know and ;

[0095] (8)

[0096] (9)

[0097] (13) According to the ratio of worker trust and offer, the sorted results are given to the data requester;

[0098] (14) The data requester selects workers according to the trust state of the workers, and selects workers from high to low according to the ratio

[0099] (15) Inform the worker to execute the task, and the worker executes the task; Set the data perceived by the first worker as , and a total of workers perceive data;

[0100] (16) Each worker generates two random numbers, and the random numbers generated by the first worker are and ; Each worker uploads the two random numbers generated by himself to the data requester; Then, and the data is added, multiplied, and uploaded and to the server, and the server to 2 data;

[0101] (10)

[0102] (11)

[0103] (17) The server interacts with the data requester to get the weight, as follows:

[0104] (a) The server selects a data as ;

[0105] (b) Save the true value of the previous round , calculate the distance between each worker and ;

[0106] (12)

[0107] (c) The server sends the difference value of each worker to the data requester, and the data requester receives difference values of workers, calculates the real and​​​​ The distance;

[0108] = (12)

[0109] Calculate the weight of each worker using the following formula:

[0110] (13)

[0111] The data requester then sends each worker's Send to the server;

[0112] (d) The server allows Calculate using the following formula

[0113] (14)

[0114] (e) If The value is greater than the threshold If the condition is met, return to step (b), loop, and continue; otherwise, proceed with the calculation.

[0115] (18) The data requester updates the worker's trust level.

[0116] (a) The data requester will perceive the task's The workers belong to the set The average weights of trusted workers are calculated for the perception task. The workers belong to the set The set of trustworthy workers is The number of sets is ;

[0117] | refers to a set Workers in the middle (15)

[0118] (b) Calculation not in set The weight of each worker in the middle and difference rate As shown in the following formula:

[0119] (16)

[0120] (c) If Less than the threshold Then, according to formula 17, worker trust can be increased. Conversely, according to formula 18, worker trust will decrease. ;

[0121] (17)

[0122] (18)

[0123] (d) if the updated worker is greater than the upper threshold , the worker is assigned to the trusted worker set ; if the updated worker is less than the lower threshold , the worker is assigned to the malicious worker set ;

[0124] (19) reassign the weight of the worker according to the trust degree of the worker: if the worker is assigned to the malicious worker set , the weight of the worker is 0. Other than that, the weight of the worker is its trust degree. Thus the weight update is summarized as follows:

[0125] (19)

[0126] (20) the data requester sends the of each worker to the server; for

[0127] (21) the server calculates according to the following formula: the server sends to the data requester, and the obtained by the data requester is the final true value.

[0128] .

[0129] Embodiment 2

[0130] The difference between this embodiment and embodiment 1 is only as follows:

[0131] In the crowd sensing network, in the case of needing to obtain real data of a place and requiring high data quality, such as sensing application scenarios of temperature and humidity, air quality, traffic conditions and the like, and uploading the sensed data to a platform for processing. In the task publishing of the platform, participants apply to participate in the task, and different data provided by the workers. The method of the application can improve the accuracy of the data in the case of privacy protection, without leaking any data content and bid of the worker to any party, and identifying the trustworthiness of the worker.

[0132] The experimental results of the method of the application are given below.

[0133] Figure 1 ​The network structure diagram of the method of the application is given.The method of the application has the following components: the network structure is composed of a data requester, a data requester representation, a server and a block chain.The block chain is mainly used to obtain the ratio of the trust degree of the worker to the bid, thereby guiding the selection of the worker;the server and the data requester cooperatively calculate the true value under the condition of privacy protection;and different from the general true value calculation method, the server and the data requester perform trust reasoning in the process of calculating the true value, thereby being capable of identifying the trust degree of the worker and adjusting the weight of the worker according to the trust degree, and obtaining a more accurate true value.

[0134] Figure 2 The experimental results of the data accuracy of the method of the application and other methods are given, and the methods compared with the method of the application are three: the first one is CRH, which is a widely used method, recruits workers to execute a perception task, and then, the weighted average of the obtained data is the final result;the weight of the worker is related to the distance between the data reported by the worker and the final result, if the data of the worker is far from the final result, the weight is small, and vice versa;the second method is the average method, that is, recruits workers to execute a perception task, and then, the average of the obtained data is the final result;the last one is the median method, that is, the final result takes the median data.From the experimental results in Table 1, it can be seen that the method of the application can effectively reduce the error.

[0135] Figure 3 The performance of the method of the application in identifying the trust degree of the worker is given, and from the figure, it can be seen that for the trusted worker, the trust degree continuously rises to above the threshold of the trusted worker;and for the malicious worker, the trust degree continuously decreases.It can be seen that the method of the application has good ability to identify the trust degree of the worker, and can guide the selection of the trusted worker in the worker selection, thereby improving the data quality.

[0136] The experimental results show that the application performs well in data quality, and in the comparison of the method of the application with other methods, compared with the CRH method, the average method and the median method, the average absolute error (MAE) of the data of the method of the application is reduced by the amplitude shown in Table 1, and it can be seen that the method of the application can effectively reduce the data error.

[0137] Table 1: The amplitude of the reduction of the average absolute error of the data of the method of the application relative to other methods

[0138]

[0139] The reason for producing such an effect is that the method can effectively identify the credibility of the worker, thereby guiding the selection of workers with high trust and low price to participate in data collection, and improving the data quality.

[0140] The various embodiments are described in the present specification in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be mutually referred to. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0141] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the 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 present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A low-cost high-quality data acquisition method with trust and privacy protection, characterized in that, The method comprises the following steps: A data requester publishes a task on a data transaction center; Workers apply to perform the task according to their own will, and encrypt and upload their task offer to the blockchain of the data transaction center by using their public keys; The data requester removes the workers with a trust degree less than a preset threshold from the worker set applying for the task, and sends the ciphertext of the trust degrees of the remaining workers to the data transaction center by using the public key of the data requester; The data requester generates a re-encryption key by using the public key of the server and the private key of the data requester, and sends the re-encryption key to the server; The server generates a random number for each worker and encrypts it with the worker's public key and sends it to the data exchange center; （3） The server encrypts the data by using the re-encryption key, and decrypts the data by using the private key of the server to obtain the ratio of the trust degree and the offer of the worker, specifically as follows: （4） wherein, represents a bid encrypted ciphertext; worker first using the data requester's public key with his generate a re-encryption key to the data transaction center; wherein, for the worker private key; the data transaction center will re-encryption, converted into the data requester's public key encrypted ciphertext: (5) The data transaction center will With In the encrypted state, the following operations are performed; （6） The data transaction center will send to the server, the server in the encryption state to calculate : （7） Data requester uses server's public key With his own Generate re-encryption key To the server, the server uses the re-encryption key Encrypted into his own public key encryption, and then use his private key decryption as formula 9, get the ratio of worker trust and offer; Where, Is the private key of the data requester; （8） （9） According to the ratio of the worker's trust degree and the bid, the sorted result is given to the data requester, the data requester selects the worker according to the trust state of the worker, and selects the worker from high to low according to the ratio The worker is informed to execute the task, and the worker executes the task.

2. The method of claim 1, wherein, For each worker , a cyclic group is generated with order ; a worker selects a from the set , and then calculates ; the public key generated by each worker is and is public, and the private key is , which is not public and is kept by the worker ; the public key of the worker is represented by and represent the public keys of the data requester and the server respectively, and , represent the private keys of the data requester and the server respectively; Worker set represents, trusted worker set represents, initial trust of workers in the set is 1; the trust of the remaining other workers is unknown represents, trust of 0.5; the trust of workers is between [0, 1], 0 represents low trust, 1 represents the highest trust Malicious worker set is initially empty; the trust level of a worker is .

3. The method of claim 1, wherein, Workers use their public key to make offers for tasks they can do Uploaded to the blockchain encrypted, the expression is as follows: （1） representing the worker public key, representing the offer encrypted ciphertext, representing encrypting with the key to perform.

4. The method of claim 1, wherein, The data requester removes the workers with the trust degree less than the preset threshold from the worker set applying for the task, and sends the ciphertext generated by encrypting the trust degrees of the remaining workers using the public key to the data transaction center, and the expression is as follows: the data requester removes the workers with the trust degree less than the preset threshold from the worker set applying for the task, and sends the ciphertext generated by encrypting the trust degrees of the remaining workers using the public key to the data transaction center The workers with the trust degree less than the threshold The workers with the trust degree less than the threshold The workers with the trust degree less than the threshold The workers with the trust degree less than the threshold The workers with the trust degree less than the threshold （2）。 5. The method of claim 1, wherein, The homomorphic encryption performance calculation of the smart contract on the blockchain is multiplication with a constant: The server selects one data as ; Save the true values from the previous round = , calculate the distance of each worker from ; = （12） The server sends the difference for each worker to the data requester, who receives the difference for each worker, and calculates the true distance from the server to the data requester using the following formula: = （12） After the workers complete the task, the server and the data requester interact to obtain the weight, and the specific steps are as follows: （13） The data requester then sends to the server a request for each worker's data; The server lets , the following equation is used （14） if The value is greater than the threshold Then return to calculating each worker and The distance, the cycle, continues.

6. The method of claim 1, wherein, The weight of each worker is calculated according to the following formula: After the workers complete the task, the data requester updates the trust degree of the workers, and the specific steps are as follows: The data requester will average the weights of the trusted workers to which the perception task belongs The number of sets to which the worker belongs The number of sets to which the worker belongs The number of sets to which the worker belongs The number of sets to which the worker belongs The number of sets to which the worker belongs ; | refers to the workers (15) in the set ​ Computing not in set the difference rate of the weight of each worker from the average as follows: （16） If less than threshold then raise the trust level of the worker by equation (17) otherwise lower the trust level of the worker by equation (18) ; （17） （18） If the updated worker is greater than an upper threshold , the worker is classified as a trusted worker ; if the updated worker is less than a lower threshold , the worker is classified as a malicious worker ; Re-assigning weights to workers based on their trustworthiness: if a worker is classified as a malicious worker , then the weight of the worker , and other workers except for this one, is their trustworthiness; thus the weight update is calculated as follows: （19）。 7. The method of claim 6, wherein, The server calculates according to the following formula The server sends to the data requester, and the data requester gets as the final true value: 。

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