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ISPRS Int. J. Geo-Inf. 2017, 6(6), 163; doi:10.3390/ijgi6060163

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

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1,2,3,4,* , 1,2
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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
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College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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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
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)
View Full-Text   |   Download PDF [2339 KB, uploaded 21 June 2017]   |  

Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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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