EPLA: Efficient Personal Location Anonymity

  • Dapeng Zhao
  • Kai Zhang
  • Yuanyuan Jin
  • Xiaoling Wang
  • Patrick C. K. Hung
  • Wendi Ji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)


A lot of researchers utilize side-information, such as 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. 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 selected the dummy locations to achieve k-anonymity according to personal visit probability for users’ queries. AKDE greatly reduces the computational complexity compared with KDE approach. We conduct comprehensive experimental study on the realistic Gowalla data sets and the experimental results show that EPLA obtains fine privacy performance and efficiency.


LBS Privacy Anonymity KDE Cloaking region 



This work was supported by NSFC grants (No. 61532021, 61472141 and 61021004), Shanghai Knowledge Service Platform Project (No. ZF1213), Shanghai Leading Academic Discipline Project (Project NumberB412) and Shanghai Agriculture Science Program (2016) Number 2-1.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dapeng Zhao
    • 1
  • Kai Zhang
    • 1
  • Yuanyuan Jin
    • 1
  • Xiaoling Wang
    • 1
  • Patrick C. K. Hung
    • 2
  • Wendi Ji
    • 1
  1. 1.Shanghai Key Laboratory of Trustworthy Computing, Institute for Data Science and EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Faculty of Business and Information TechnologyUniversity of Ontario Institute of Technology (UOIT)OshawaCanada

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