, Volume 22, Issue 1, pp 29–47 | Cite as

EPLA: efficient personal location anonymity

  • Dapeng Zhao
  • Yuanyuan Jin
  • Kai Zhang
  • Xiaoling WangEmail author
  • Patrick C. K. Hung
  • Wendi Ji


A lot of researchers utilize side-information, such as the map which is likely to be exploited by some attackers, to protect users’ location privacy in location-based service (LBS). However, current technologies universally model the side-information for all users and don’t distinguish different users. We argue that the side-information is personal for every user. In this paper, we propose an efficient method, namely EPLA, to protect the users’ privacy using visit probability. We select the dummy locations to achieve k-anonymity according to personal visit probability for users’ queries. In EPLA, we use AKDE(Approximate Kernel Density Estimate), which greatly reduces the computational complexity compared with KDE approach. We conduct the comprehensive experimental study on the two real Gowalla and Foursqure data sets and the experimental results show that EPLA obtains fine privacy performance and low computation complexity.


LBS Privacy Anonymity KDE Cloaking region 



This work was supported by NSFC grants (No. 61532021 and 61472141), Shanghai Knowledge Service Platform Project (No. ZF1213), Shanghai Leading Academic Discipline Project (Project NumberB412) and Shanghai Agriculture Applied Technology Development Program (GrantNo.G20160201).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.International Research Center of Trustworthy Software, Shanghai Key Laboratory of Trustworthy ComputingEast China Normal UniversityShanghaiChina
  2. 2.Faculty of Business and Information TechnologyUniversity of Ontario Institute of Technology(UOIT)OshawaCanada

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