A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing
In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt th...
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doaj-facfc83ba8e04c5d8062dd60710717302020-11-25T03:27:55ZengHindawi-WileySecurity and Communication Networks1939-01141939-01222020-01-01202010.1155/2020/88929548892954A Dynamic Privacy Protection Mechanism for Spatiotemporal CrowdsourcingTianen Liu0Yingjie Wang1Zhipeng Cai2Xiangrong Tong3Qingxian Pan4Jindong Zhao5School of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaDepartment of Computer Science, Georgia State University, Atlanta 30303, GA, USASchool of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaIn spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k-anonymity and l-diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k-anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data.http://dx.doi.org/10.1155/2020/8892954 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tianen Liu Yingjie Wang Zhipeng Cai Xiangrong Tong Qingxian Pan Jindong Zhao |
spellingShingle |
Tianen Liu Yingjie Wang Zhipeng Cai Xiangrong Tong Qingxian Pan Jindong Zhao A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing Security and Communication Networks |
author_facet |
Tianen Liu Yingjie Wang Zhipeng Cai Xiangrong Tong Qingxian Pan Jindong Zhao |
author_sort |
Tianen Liu |
title |
A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing |
title_short |
A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing |
title_full |
A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing |
title_fullStr |
A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing |
title_full_unstemmed |
A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing |
title_sort |
dynamic privacy protection mechanism for spatiotemporal crowdsourcing |
publisher |
Hindawi-Wiley |
series |
Security and Communication Networks |
issn |
1939-0114 1939-0122 |
publishDate |
2020-01-01 |
description |
In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k-anonymity and l-diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k-anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data. |
url |
http://dx.doi.org/10.1155/2020/8892954 |
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