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EPLA: efficient personal location anonymity

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Abstract

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.

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Acknowledgment

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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Authors and Affiliations

  1. International Research Center of Trustworthy Software, Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China

    Dapeng Zhao, Yuanyuan Jin, Kai Zhang, Xiaoling Wang & Wendi Ji

  2. Faculty of Business and Information Technology, University of Ontario Institute of Technology(UOIT), 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canada

    Patrick C. K. Hung

Authors
  1. Dapeng Zhao
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  2. Yuanyuan Jin
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  3. Kai Zhang
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  4. Xiaoling Wang
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  5. Patrick C. K. Hung
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  6. Wendi Ji
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Correspondence to Xiaoling Wang.

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Zhao, D., Jin, Y., Zhang, K. et al. EPLA: efficient personal location anonymity. Geoinformatica 22, 29–47 (2018). https://doi.org/10.1007/s10707-017-0303-4

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  • DOI: https://doi.org/10.1007/s10707-017-0303-4

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