Privacy and location anonymization in location-based services
SIGSPATIAL Special
Achieving efficient query privacy for location based services
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
A taxonomy of approaches to preserve location privacy in location-based services
International Journal of Computational Science and Engineering
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
On the limitations of query obfuscation techniques for location privacy
Proceedings of the 13th international conference on Ubiquitous computing
Privacy preservation in the dissemination of location data
ACM SIGKDD Explorations Newsletter
Cover locations: availing location-based services without revealing the location
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Privacy Preserving Cloud-Based Computing Platform (PPCCP) for Using Location Based Services
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Protecting users' location information in location-based services, also termed location privacy, has recently garnered significant attention due to its importance in satisfying users' privacy concerns when using location-aware services. Several approaches proposed in the literature blur the user's location in a region by increasing its spatial extent or anonymizing the user among several other users. Such approaches in nature require users to communicate through a trusted anonymizer for all of their queries which can impose unrealistic overall communication/computation overhead between the server and the anonymizer for users with more stringent privacy requirements. We revisit the location privacy problem with the objective of providing significantly more stringent privacy guarantees and propose SPIRAL, a Scalable Private Information Retrieval Approach to Location privacy, which is to the best of our knowledge, the first approach to utilize practical Private Information Retrieval (PIR) as a more fundamental approach to enable blind evaluation of range queries. We perform several experiments on real-world data to evaluate the effectiveness and the feasibility of our approach.