Abstract
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.
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Acknowledgment
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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Zhao, D., Zhang, K., Jin, Y., Wang, X., Hung, P.C.K., Ji, W. (2016). EPLA: Efficient Personal Location Anonymity. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_21
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DOI: https://doi.org/10.1007/978-3-319-45817-5_21
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