A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing
With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can eff...
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Online Access: | http://dx.doi.org/10.1155/2021/2296386 |
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doaj-d4ae9884f8214fd79a2d1a61a441649f2021-07-05T00:01:42ZengHindawi-WileySecurity and Communication Networks1939-01222021-01-01202110.1155/2021/2296386A Secure Truth Discovery for Data Aggregation in Mobile Crowd SensingTaochun Wang0Chengmei Lv1Chengtian Wang2Fulong Chen3Yonglong Luo4School of Computer and InformationSchool of Computer and InformationSchool of Computer and InformationAnhui Provincial Key Laboratory of Network and Information SecuritySchool of Computer and InformationWith the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.http://dx.doi.org/10.1155/2021/2296386 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Taochun Wang Chengmei Lv Chengtian Wang Fulong Chen Yonglong Luo |
spellingShingle |
Taochun Wang Chengmei Lv Chengtian Wang Fulong Chen Yonglong Luo A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing Security and Communication Networks |
author_facet |
Taochun Wang Chengmei Lv Chengtian Wang Fulong Chen Yonglong Luo |
author_sort |
Taochun Wang |
title |
A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing |
title_short |
A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing |
title_full |
A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing |
title_fullStr |
A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing |
title_full_unstemmed |
A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing |
title_sort |
secure truth discovery for data aggregation in mobile crowd sensing |
publisher |
Hindawi-Wiley |
series |
Security and Communication Networks |
issn |
1939-0122 |
publishDate |
2021-01-01 |
description |
With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security. |
url |
http://dx.doi.org/10.1155/2021/2296386 |
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