Research On Trajectory Privacy Metric In Location-based Services

The characteristic of location-based services is that the user must provide his/her location information before enjoying the services, which easily results in the exposure of the user's location privacy. Trajectory privacy is special location privacy. Once the user's trajectory privacy is exposed, then his more personnel privacy information will be threatened. Various trajectory privacy preserving methods can protect user's trajectory privacy on some extent, but privacy still be exposed when these methods are applied in practice. Therefore, it is needed to establish effective trajectory privacy metric to evaluate the effect of these trajectory privacy preserving methods.This paper proposes new trajectory privacy metric for Silent Cascade which is a prevalent trajectory privacy preserving method in LBS (location-based services). In this metric, user's trajectory is modeled as a weighted undirected graph, trajectory privacy metric is depicted with uncertainty, and the user's trajectory privacy level is quantized as the probability of the relevance between the user's pseudonym before and after each mix-zone and calculated using 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 metric by assuming the adversary has three classes of different background knowledge in each metric process. The privacy metric result composes of the assumptive background knowledge and the corresponding trajectory privacy level. Conditional probability is proposed to describe adversarial background knowledge, and (KUL(Ki+,Ki-),KL(Ki+,Ki-)) association rule is also proposed to quantify 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. The user's privacy is protected well when the attacker has no background knowledge, but is becoming exposed when the attacker has more background knowledge. In addition, the measure of describing and quantizing adversary background knowledge is effective, which helps the users have intuitionist and digitized understanding of the corresponding trajectory privacy level on the effect of the assumptive background knowledge when they are enjoying the service.