Towards Modeling Privacy in WiFi Fingerprinting Indoor Localization and its Application

In this paper, we study privacy models for privacy-preserving WiFi fingerprint based indoor local- ization (PPIL) schemes. We show that many existing models are insufficient and make unrealistic assumptions regarding adversaries’ power. To cover the state-of-the-art practical attacks, we propose the first formal security model which formulates the security goals of both client-side and server-side privacy beyond the curious-but-honest setting. In particular, our model considers various malicious behaviors such as exposing secrets of principles, choosing malicious WiFi fingerprints in location queries, and specifying the location area of a target client. Furthermore, we formulate the client-side privacy in an indistinguishability manner where an adversary is required to distinguish a client’s real location from a random one. The server-side privacy requires that adversaries cannot generate a fab- ricate database which provides a similar function to the real database of the server. In particular, we formally define the similarity between databases with a ball approach that has not been formalized before. We show the validity and applicability of our model by applying it to analyze the security of an existing PPIL protocol. We also design experiments to test the server-privacy in the presence of database leakage, based on a candidate server-privacy attack.Peer reviewe