A new ‐map mechanism for mobility traces privacy

Abstract

A major concern of the deployment of location based services (LBSs) is the safeguards of the user's location data collected by service providers, since personal location data may imply sensitive private information. However, most available works proposed so far rely on syntactic privacy models such as k-anonymity and location perturbation, which are proved to quiver in the balance with privacy and usability requirements. In this article, we provide a new-map mechanism to help users better understand the privacy/accuracy tradeoff process and preserve location data. In our-map model, the user can specify a geographic region to hide her precise location and suppress the following queries in the same area to meet her privacy requirement, while maintaining and understanding its usability. In addition, we propose a new notion of Δt-privacy based on differential privacy to account for the temporal-spatial correlation and history correlation in the case of crossing region, which is the major privacy concern of a moving user's trace. Finally, we evaluate our framework by using an online LBS with real-world data sets. The results not only indicate that the-map is significantly useful for identifying the privacy and utility tradeoffs but also show the effectiveness and practicality of the proposed Δt-privacy.