EPLA:Efficient Personal Location Anonymity

摘要：

　　A lot of researchers utilize side-information,such as map which is likely to be exploited by some attackers,to protect userslocation 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 usersprivacy using visit probability.We selected the dummy locations to achieve k-anonymity according to personal visit probability for usersqueries.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.

关键词： LBS Privacy Anonymity KDE Cloaking region

作者: Dapeng Zhao Kai Zhang Yuanyuan Jin Xiaoling Wang Patrick C.K.Hung Wendi Ji

作者单位: Shanghai Key Laboratory of Trustworthy Computing,Institute for Data Science and Engineering,East Chi Faculty of Business and Information Technology,University of Ontario Institute of Technology(UOIT),O

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 263-275

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）