Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
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Countless challenges to preserving a user’s location privacy exist and have become more important than ever before with the proliferation of handheld devices and the pervasive use of Location-based Services. It is not possible to access Location-based services and, at the same time, to preserve privacy when the user provides his exact location information. To achieve privacy, most third party based systems use a location anonymizer to achieve k-anonymity so that the user remains indistinguishable among k-1 other requesters. In this paper, we present a novel approach called ELALPS to preserve location privacy without any intermediate location anonymizer. Our framework uses a new concept, namely, Landmark Influence Space (LIS) that proves to be efficient in location anonymization and query processing. The framework is complemented by a collaboration-based light-weight k-anonymity protocol that does not require standard cryptographic operations and trust formation among users. Evaluation shows that our system has been able to bridge the gap between privacy and performance.