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Open AccessArticle

Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain

by Hui Wang 1,2, Haiping Huang 1,2,3,4,*, Yuxiang Qin 1,2, Yunqi Wang 1,2 and Min Wu 1,2,3
1
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
3
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
4
Institute of Computer Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Academic Editors: Chi-Hua Chen, Kuen-Rong Lo and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(6), 163; https://doi.org/10.3390/ijgi6060163
Received: 9 December 2016 / Revised: 24 May 2017 / Accepted: 30 May 2017 / Published: 1 June 2017
(This article belongs to the Special Issue Applications of Internet of Things)
Currently, although prevalent location privacy methods based on k-anonymizing spatial regions (K-ASRs) can achieve privacy protection by sacrificing the quality of service (QoS), users cannot obtain accurate query results. To address this problem, it proposes a new location privacy-preserving k-anonymity method based on the credible chain with two major features. First, the optimal k value for the current user is determined according to the user’s environment and social attributes. Second, rather than forming an anonymizing spatial region (ASR), the trusted third party (TTP) generates a fake trajectory that contains k location nodes based on properties of the credible chain. In addition, location-based services (LBS) queries are conducted based on the trajectory, and privacy level is evaluated by instancing θ privacy. Simulation results and experimental analysis demonstrate the effectiveness and availability of the proposed method. Compared with methods based on ASR, the proposed method guarantees 100% QoS. View Full-Text
Keywords: k-anonymity; location-based services; location privacy; the credible chain k-anonymity; location-based services; location privacy; the credible chain
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Wang, H.; Huang, H.; Qin, Y.; Wang, Y.; Wu, M. Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain. ISPRS Int. J. Geo-Inf. 2017, 6, 163.

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