CN114742442A - Trust-based participant selection method for improving data quality - Google Patents

Trust-based participant selection method for improving data quality Download PDF

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CN114742442A
CN114742442A CN202210455504.7A CN202210455504A CN114742442A CN 114742442 A CN114742442 A CN 114742442A CN 202210455504 A CN202210455504 A CN 202210455504A CN 114742442 A CN114742442 A CN 114742442A
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曲振哲
李泽源
刘安丰
陆嘉恒
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Abstract

The invention discloses a trust-based participant selection method for improving data quality. A method for identifying the trust degree of participants and selecting the trusted participants to collect data is provided for the condition that low-trusted participants submit false data in a crowd-sourcing network. The method is based on the fact that a small number of high-credibility participants exist in the beginning of a platform, then, a plurality of participants are selected for the same task when the participants are selected, on one hand, data are collected, and on the other hand, the credibility of the participants is evaluated. The evaluation method is to compare the data of the participants with unknown trust with the data of the high-trust and trusted participants, and identify whether the collected data is real to evaluate the trust. When the number of trusted participants is less, the system automatically increases the number of the participants for data acquisition, thereby increasing the trust degree of evaluating more participants, and when the trusted participants reach the available state, the number of the participants for evaluation is reduced, thereby saving the cost. Finally, the purposes of improving the data acquisition quality and saving the cost are achieved.

Description

Trust-based participant selection method for improving data quality
Technical Field
The invention belongs to the field of credible data collection of a crowd sourcing network, and particularly relates to a method for acquiring credibility of participants and collecting real data in the crowd sourcing network.
Background
The crowd sensing network is a novel network, and the data collection mode of the crowd sensing network is a participation mode. Namely, the place of the system platform for releasing the collected data, the content of the data collection, the reward given to the data participants and other information. And the data participant mainly refers to a person holding the mobile phone or other sensing equipment. Data participants perceive the data through the mobile phone and then submit the data to the platform to obtain a reward. Because the number of data participants is large, the crowd sensing network can obtain data in a long time, a large range and at a low cost. The data submitted by the data participants directly influences the quality of the platform construction application. Therefore, the platform is required to select credible participants with high collected data quality for data collection. As the participant-perceived data consumes resources such as computation, storage, communication resources, and time. And the higher the perception quality, the more the requirements on the participants are, the more resources and time are paid by the participants, and some tasks need to move to a specified place to be perceived, so the higher the cost is paid. The platform thus needs to compensate the participants for the cost of completing their tasks. However, there are some participants in crowd-sourcing aware networks with low confidence, even malicious, who want to earn the most rewards at the lowest possible cost. For this reason, these participants often do not collect data, but compile reports of false data to the platform, which costs the least and can be paid a large reward. And the malicious participants not only want to obtain a large reward but also use the reported malicious data to attack the platform, so that the system is subjected to more loss than the false data. For example, navigation based on erroneous data, weather forecasts can disorient customers, lose life in harsh environments, and even occur during large field sports. It is seen that there is a pressing need for an efficient way to select trusted participants to report high quality data to build high quality applications. The most crucial element in the quality of the data is to ensure the authenticity of the data, i.e. to require the participant to report data that is consistent with the actual data, also called true image discovery, or actual data discovery. There have been some studies of real data discovery. The research mainly obtains real data from obtained data through a calculation method, and mainly relates to a method based on mathematical calculation, such as an average value method, a median method, a weighted average method and the like. The main characteristic of the method is that n participants are simultaneously selected to perceive the same perception target. Then, the n numbers are averaged to obtain an average value, a median, and a weighted average as real data. The basic idea of adopting the method is that most participants in the crowd sensing network are considered to be credible, and the data reported by the credible participants is real. Therefore, if averaging or the like is performed on n data, the influence of even dummy data existing therein can be reduced. It can be seen that in this type of method, it is not known exactly what the actual data is, nor is the result accurate. And the method is easy to attack, and if a plurality of malicious participants jointly attack, the false data expected by the attacker can be obtained by the platform. The method of the invention provides an effective identification of the credibility of the participants, so that the credible participants are selected to sense the data, and the high-quality crowd sensing application can be constructed.
Disclosure of Invention
The invention discloses a method for selecting participants to improve data quality based on trust. The method of the invention provides a method for identifying the trust degree of participants aiming at the condition that the participants in the crowd-sourcing network have low credibility in data collection or the malicious participants submit false or malicious data, and then selecting the credible participants to collect data according to the trust degree of the participants, thereby improving the data collection quality. The participator is independent and the person of the platform holding the sensing equipment such as the mobile phone and the like voluntarily senses the data and submits the data to get the reward. The method can obtain a large amount of data quickly and at low cost. However, the development of crowd-sourcing network applications is plagued by the fact that it is difficult for the platform to verify whether the data submitted by the participants is authentic. Therefore, the method of the present invention is to provide a low-cost and effective method for identifying the trust level of the participant and then selecting the perception data of the trusted participant to achieve the above-mentioned goal. The method provided by the invention starts to have a small part of high-credibility participants based on an initial platform, then selects a plurality of participants for the same data acquisition task when selecting the participants, on one hand, acquires data, on the other hand, evaluates the credibility of the participants, and the method compares the data of the participants with unknown credibility with the data of the high-credibility and credibility participants, thereby effectively identifying whether the data participating in the acquisition is real or not and evaluating the credibility of the data. When the system obtains fewer trusted participants (step (3) of the claim), the system automatically increases the number of the participating data collectors, thereby increasing the times of evaluating the trust of the data participants and obtaining more trusted participants for data collection, and when the network obtains the available state of the trusted participants, the number of the participating participants is reduced, thereby saving the cost. Finally, the purposes of improving the data acquisition quality and saving the cost are achieved.
The technical solution of the invention is as follows:
1. a method for selecting and improving data quality based on trust participants is characterized by comprising the following steps:
(1) the system platform issues tasks needing to collect n place data, and after data participants in the network know the tasks of collecting the data, m participants apply for data collection to the platform;
(2) after receiving the application of m participants, the system platform selects the participants according to the following method:
for each sampling point diThe following operations are performed
If the sampling point diIf the participants with high credibility can be selected, selecting 1 participant with high credibility as a data collector;
otherwise, e.g. sample point diIf the trusted reference person can be selected, k trusted participants with the trust degrees larger than the threshold phi are selected as data collectors;
otherwise, selecting s confidence degrees larger than a threshold value according to the confidence degree
Figure BDA0003620331210000023
The participant of (2) is a data collector; if the number of the selected participants is less than s, the rest participants with unknown trust degree are selected;
(3) if the proportion of the credible or high-credible participants selected from the n places is smaller than the set threshold value in the method
Figure BDA0003620331210000022
Then increase the selection
Figure BDA0003620331210000021
The method comprises the following steps of (1) evaluating the credibility of each participant with unknown credibility:
firstly, selecting the participants with unknown credibility from the sampling points of the participants with high credibility, and if the number of the selected participants with unknown credibility reaches the number
Figure BDA0003620331210000031
Then the process is finished; otherwise, the participants with the unknown credibility are selected from the sampling points of the participants with the credibility by the participants with the unsatisfied number until the selected number reaches the limit value
Figure BDA0003620331210000032
Then it is complete. If not enough to pick
Figure BDA0003620331210000033
The same is true for the individual participants whose trust levels are unknown.
(5) The platform informs the selected participants of collecting data, and the participants perceive the data and submit the data to the platform;
(6) after the platform receives the data of all the selected participants, the platform calculates the data value as the final data value of the platform as follows.
If the sampling point diIf 1 or more high-credibility participants exist, the true data value of the sampling point is the average value of the high-credibility participants, and the values of other participants are not considered;
if the sampling point diIf no high-credibility participant exists and 1 or more credibility participants exist, the true data value of the sampling point is the average value of the credibility participants, and the values of other participants are not considered;
if the sampling point diNo high credibility and credibility participants exist, and the credibility is larger than a threshold value
Figure BDA00036203312100000310
The known participant of (2) then selects a confidence level greater than the thresholdValue of
Figure BDA0003620331210000038
The average value of the data of the participants is the data true value of the sampling point and is the average value of the credible participants, and the values of other participants are not considered;
if the sampling point diNo confidence level greater than the threshold
Figure BDA0003620331210000039
If the participant is known, the true data value of the sampling point is the average value of all participants with unknown trust;
(7) in the above process, the calculation method of the trust level of the participant is as follows:
the high-credibility participants are obtained by the platform through an external method, the participants completely and truly acquire data, the credibility of the participants is always 1, and calculation is not needed;
the credible participant obtains that the threshold value of the credibility is larger than the threshold value through the following calculation method
Figure BDA0003620331210000037
Phi is larger, for example, 0.9; and the threshold value
Figure BDA0003620331210000035
Is less than
Figure BDA0003620331210000036
Such as 0.65;
the credibility of other participants is unknown at the beginning except that the credibility of the high credible participant is 1, and the credibility of the high credible participant is 0.5 at the moment;
for each sampling, the following confidence calculations were performed: when sampling point diWhen the participant with high credibility exists in the system, the comparison result of the data collected by other participants and the data collected by the participant with high credibility is in the error range, and the successful collection times A of the ith participanti,tIncreasing for 1 time; if the sampling point d is presentiIf there is no high credible participant, and if there is credible participant, the data and credibility collected by other participantsComparing the highest credible participants, if the comparison result of the data is within the error range, Ai,tIncreasing for 1 time; the contrast alignment accuracy of the ith participant at time t is calculated as follows, wherein Ai,totIs the total number of comparisons
Figure BDA0003620331210000034
Ci,tRepresents the overall confidence of the ith participant at time t, and Ci,t-1Representing the participant's confidence level at time t-1. Ci，0Initial value representing the total credibility of the ith participant, wherein the total credibility can be increased maximally every time of updating
Figure BDA0003620331210000041
(when σ isrWhen 1). Threshold value sigmasIs defined to decide whether to perform trust increase or decrease, e.g. sigmasA value of 0.6 indicates that the probability of the participant collecting data accurately in the time period t is 0.6, and if the probability is greater than 0.6, the participant is considered to be credible, so that the credibility is increased. SigmarGreater than sigmasρ is a variable greater than 1, and is used to control how fast each confidence update is. If σ isrGreater than sigmasThen, the confidence calculation formula for updating the participants is as follows:
Figure BDA0003620331210000042
if σ isrLess than sigmasThen, the confidence calculation formula for updating the participants is as follows:
Figure BDA0003620331210000043
advantageous effects
The invention discloses a trust-based participant selection method for improving data quality. The basic idea of the inventive method is: the crowd-sourcing network platform initially knows that there is a small percentage of highly trusted participants in the network. Then, when selecting the participants, the high-credibility participants are preferentially selected, and then, a plurality of participants with unknown credibility are simultaneously selected. The purpose of selecting the high-credibility participants is to ensure that the platform collects real data, so that the current system platform obtains good effect. And a portion of participants whose trustworthiness is unknown are selected because: the platform is initially trusted by a small number of participants and is therefore forced to choose participants with unknown trust many times, possibly to choose low-trust or malicious participants, thereby compromising the system. Thus, participants with unknown trust levels are selected and data is collected and compared with data submitted by trusted participants to determine their trustworthiness. Thus, the multiple-choice, less trusted participants are the cost of the platform to obtain better revenue in the future. After this process continues, the participant that the platform can discern will be more and more, and the speed of discerning also can be faster and faster in addition, and when the credible participant that the platform can discern reached a quantitative, the platform just can select just can satisfy the data collection requirement from the credible participant, and at this moment, the platform just reduces the quantity that inspects the participant in order to reduce the cost to reach a balance: when the credible participants are not enough, the credible inspection is strengthened to ensure the quality; and when the number of the trusted participants is enough, the number of the trust verification is reduced to save the cost. Thereby achieving the design goal.
Drawings
FIG. 1 is a variation of participant confidence;
the accuracy of the data obtained by the method of FIG. 2;
Detailed Description
In order to facilitate an understanding of the invention, the invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of the invention is not limited to the specific embodiments below.
Unless otherwise defined, all terms of art used hereinafter have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention.
Unless otherwise specifically stated, various raw materials, reagents, instruments, equipment and the like used in the present invention are commercially available or can be prepared by existing methods.
Example (b):
in a smart city, the environment in the city, such as temperature, noise, traffic flow and the like, needs to be monitored in real time for data perception, the perceived data is uploaded to a platform for processing, and the temperature, noise and traffic flow distribution information is issued in real time, so that the smart city is guided and beneficial to the life of people. At this time, there are a large number of participants in the city, so that data can be perceived anywhere at any time and sent to the platform. But there are some untrusted users among the participants that may report virtual or malicious data making the application of the platform unreliable. Therefore, a method for acquiring and evaluating the trust of the reference in real time and high efficiently is urgently needed, so that the platform obtains high-quality real data.
The experimental results of the inventive method are given below.
The experimental results of fig. 1 were made in an experimental environment in which participants were identified as 3 types, σrRepresenting the attribute, σ, of the participant itselfr<0.4 represents a less trustworthy participant,. sigma. -%r>0.6 represents trusted participants and others represent moderately trusted participants. The participants act accordingly according to their attributes. Untrusted participants report false data primarily, while trusted participants report true data. As can be seen from the experimental results shown in FIG. 1, in the method of the present invention, with the operation of the system, the credibility of the credible participants is continuously increased, and the credibility of the participants with low reliability is decreased to be very low. Therefore, the credibility of the credible participants and the credible participants with low credibility can be identified by the provided method, so that the participants with low credibility can be guided not to be selected when the participants are selected, and the data quality of the system is high.
Fig. 2 shows the accuracy of the comparison between the data obtained by the method of the present invention and the actual data, and it can be seen that the average accuracy of the data obtained by the method of the present invention is 98%, and F1 has small deviation from the accuracy and almost keeps the same without being interfered by the obtained quantity.

Claims (1)

1. A method for selecting and improving data quality based on trust participants is characterized by comprising the following steps:
(1) the system platform issues tasks needing to collect n place data, and after data participants in the network know the tasks of collecting the data, m participants apply for data collection to the platform;
(2) after receiving the applications of m participants, the system platform selects the participants according to the following method:
for each sampling point diThe following operations are performed
If the sampling point diIf the participants with high credibility can be selected, selecting 1 participant with high credibility as a data collector;
otherwise, e.g. sample point diIf the credible reference persons can be selected, k credible participants with the credibility greater than the threshold phi are selected as data collectors;
otherwise, selecting s confidence degrees larger than the threshold value according to the confidence degrees
Figure FDA0003620331200000011
The participant of (2) is a data collector; if the number of the selected participants is less than s, the rest participants with unknown trust degree are selected;
(3) if the proportion of the credible or high-credible participants selected from the n places is smaller than the set threshold value in the method
Figure FDA0003620331200000016
Then increase the selection
Figure FDA0003620331200000012
The method comprises the following steps of (1) evaluating the credibility of each participant with unknown credibility:
firstly, selecting a participant with unknown trust degree from sampling points of participants with high trust degree, and if the selected participant has the unknown trust degreeNumber of degree-unknown participants up to
Figure FDA0003620331200000014
Then the process is finished; otherwise, the participants with the unknown credibility are selected from the sampling points of the participants with the credibility by the participants with the unsatisfied number until the selected number reaches the limit value
Figure FDA0003620331200000013
Then it is complete. If not enough to pick
Figure FDA0003620331200000015
The same is true for the individual participants whose trust levels are unknown.
(5) The platform informs the selected participants of collecting data, and the participants perceive the data and submit the data to the platform;
(6) after the platform receives the data of all the selected participants, the platform calculates the data value as the final data value of the platform as follows.
If the sampling point diIf 1 or more high-credibility participants exist, the true data value of the sampling point is the average value of the high-credibility participants, and the values of other participants are not considered;
if the sampling point diIf no high-credibility participant exists and 1 or more credibility participants exist, the true data value of the sampling point is the average value of the credibility participants, and the values of other participants are not considered;
if the sampling point diNo high credibility and credibility participants exist, and the credibility is larger than a threshold value
Figure FDA0003620331200000017
The known participants are selected to have the confidence degree larger than the threshold value
Figure FDA0003620331200000018
The average value of the data of the participants is the data true value of the sampling point and is the average value of the credible participants, and the values of other participants are not considered;
if the sampling point diNo confidence level greater than the threshold
Figure FDA0003620331200000019
If the participant is known, the true data value of the sampling point is the average value of all participants with unknown trust;
(7) in the above process, the calculation method of the trust level of the participant is as follows:
the high-credibility participants are acquired by the platform through an external method, the participants completely and truly acquire data, the credibility of the participants is always 1, and calculation is not needed;
the credible participant obtains that the threshold value of the credibility is larger than the threshold value through the following calculation method
Figure FDA0003620331200000025
The value of phi is relatively large, for example, 0.9; and the threshold value
Figure FDA0003620331200000026
Is less than
Figure FDA0003620331200000027
Such as 0.65;
the credibility of other participants is unknown at the beginning except that the credibility of the high credible participant is 1, and the credibility is 0.5 at the moment;
for each sampling, the following confidence calculations were performed: when sampling point diWhen the participant with high credibility exists in the system, the comparison result of the data collected by other participants and the data collected by the participant with high credibility is in the error range, and the successful collection times A of the ith participanti,tIncreasing for 1 time; if the sampling point d is presentiIf no high credible participant exists, comparing the data collected by other participants with the credible participant with the highest credibility, and if the comparison result of the data is in the error range, Ai,tIncreasing for 1 time; the contrast alignment accuracy of the ith participant at time t is counted as follows, wherein Ai,totIs the total number of comparisons
Figure FDA0003620331200000021
Ci,tRepresents the overall credibility of the ith participant at time t, and Ci,t-1Representing the participant's confidence level at time t-1. Ci,0An initial value representing the overall credibility of the ith participant, wherein the overall credibility can be increased maximally every time the overall credibility is updated
Figure FDA0003620331200000024
(when σ isrWhen 1). Threshold value sigmasIs defined to decide whether to perform trust increase or decrease, e.g. sigmasA value of 0.6 indicates that the probability of the participant collecting data accurately in the time period t is 0.6, and if the probability is greater than 0.6, the participant is considered to be credible, so that the credibility is increased. SigmarGreater than sigmasρ is a variable greater than 1, and is used to control how fast each confidence update is. If σ isrGreater than sigmasThen, the confidence calculation formula for updating the participants is as follows:
Figure FDA0003620331200000022
if σ isrLess than sigmasThen, the confidence calculation formula for updating the participants is as follows:
Figure FDA0003620331200000023
。
CN202210455504.7A 2022-04-28 2022-04-28 Trust-based participant selection method for improving data quality Pending CN114742442A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115865476A (en) * 2022-11-29 2023-03-28 中南大学 Credible data perception method based on participant reliability and task matching
CN116070279A (en) * 2023-03-22 2023-05-05 深圳市于易点科技有限公司 Block chain-based network security information sharing method and system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115865476A (en) * 2022-11-29 2023-03-28 中南大学 Credible data perception method based on participant reliability and task matching
CN115865476B (en) * 2022-11-29 2024-04-16 中南大学 Trusted data perception method based on participant reliability and task matching
CN116070279A (en) * 2023-03-22 2023-05-05 深圳市于易点科技有限公司 Block chain-based network security information sharing method and system
CN116070279B (en) * 2023-03-22 2023-07-04 深圳市于易点科技有限公司 Block chain-based network security information sharing method and system

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