Modeling Privacy in WiFi Fingerprinting Indoor Localization

Abstract

In this paper, we study privacy models for privacy-preserving Wifi fingerprint based indoor localization (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 fabricate 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.