The location privacy issue has been addressed thoroughly so far. Cryptographic techniques, k-anonymity-based approaches, spatial obfuscation methods, mix-zones, pseudonyms, and dummy location signals have been proposed to enhance location privacy. In this chapter, the authors propose an approach, called STS (Share The Secret) that segments and distributes the location information to various, non-trusted, entities from where it will be reachable by authenticated location services. This secret sharing approach prevents location information disclosure even in situation where there is a direct observation of the target. The proposed approach facilitates end-users or location-based services to classify flexible privacy levels for different contexts of operation. The authors provide the optimal thresholds to alter the privacy policy levels when there is a need for relaxing or strengthening the required privacy. Additionally, they discuss the robustness of the proposed approach against various adversary models. Finally, the authors evaluate the approach in terms of computational and energy efficiency, using real mobile applications and location update scenarios over a cloud infrastructure, which is used to support storage and computational tasks.
Correction to: How pull vs. push information delivery and social proof affect information disclosure in location based services
