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Modeling privacy in WiFi fingerprinting indoor localization. (English) Zbl 1443.94084
Baek, Joonsang (ed.) et al., Provable security. 12th international conference, ProvSec 2018, Jeju, South Korea, October 25–28, 2018. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11192, 329-346 (2018).
Summary: 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.
For the entire collection see [Zbl 1398.94007].
MSC:
94A60 Cryptography
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