位置服务中用户轨迹的隐私度量

摘要

针对一种流行的用户轨迹隐私保护方法——Silent Cascade,提出一种新的轨迹隐私度量方法.该度量方法将用户运动轨迹用带权无向图描述,并从信息熵的角度计算用户的轨迹隐私水平.已有文献指出,当攻击者拥有新的背景知识时,任何一种隐私保护方法都会受到隐私威胁.因此,将攻击者的背景知识分级融入到度量方法中,隐私度量的结果由对背景知识的假设和相应的轨迹隐私水平值组成,并提出(KUL(Ki+Ki-).KL(Ki+Ki-))联系规则的方法来描述对背景知识的假设.模拟实验结果表明,此度量方法为移动用户和轨迹隐私保护方法的设计者提供了一个有价值的工具,能够准确地评估在攻击者具有可变背景知识情况下,用户的轨迹隐私水平.%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. (KUL(Ki+Ki-)) 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.