POSTER: Privacy-preserving Indoor Localization
Jan Henrik Ziegeldorf, Nicolai Viol, Martin Henze, Klaus Wehrle
TL;DR
This paper presents a privacy-preserving indoor localization method using Secure Two-Party Computation, balancing user privacy and system transparency with accurate room-level localization.
Contribution
It introduces a novel application of Secure Two-Party Computation to indoor localization, ensuring privacy without sacrificing accuracy.
Findings
Achieves room-level localization accuracy
Provides strong privacy guarantees
Operates with reasonable computational overhead
Abstract
Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.
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