POSTER: Privacy-preserving Indoor Localization
This addresses privacy concerns for users and providers in indoor localization systems, though it is incremental as it applies an existing cryptographic method to a specific domain.
The paper tackles the privacy conflict in WiFi-based indoor localization by using Secure Two-Party Computation to protect both user location privacy and provider model details, achieving room-level accuracy with reasonable overhead.
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.