Privacy metric for user's trajectory in location-based services

被引：3

作者：

Wang, Cai-Mei ^{[1
]}

Guo, Ya-Jun ^{[1
]}

Guo, Yan-Hua ^{[1
]}

机构：

[1] Department of Computer Science, Huazhong Normal University

来源：

Ruan Jian Xue Bao/Journal of Software | 2012年 / 23卷 / 02期

关键词：

Association rule; Background knowledge; Location-based service; Privacy metric; Trajectory privacy;

D O I：

10.3724/SP.J.1001.2012.03946

中图分类号：

学科分类号：

摘要：

This paper proposes a trajectory privacy measure for Silent Cascade, which is a prevalent trajectory privacy preserving method in LBS (location-based services). In this measure, the user's trajectory is modeled as a weighted undirected graph, and the user's trajectory privacy level is calculated through the use of information entropy. It is pointed out in literatures that any privacy preserving methods will be subject to privacy threats once the attacker has new background knowledge. Therefore, adversarial background knowledge is hierarchically integrated into this measure. The privacy metric result composes of the assumptive background knowledge and the corresponding trajectory privacy level. (K UL(K i+, K i-), K L(K i+, K i-)) association rules is also proposed to describe the assumptive background knowledge. Simulation results show that this metric is an effective and valuable tool for mobile users and the designers of trajectory privacy preserving methods to measure the user's trajectory privacy level correctly, even the attacker has variable background knowledge. © 2012 ISCAS.

引用

页码：352 / 360

页数：8

共 16 条

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