Efficient Enforcement of Privacy for Moving Object Trajectories

Abstract

Information services based on identity and current location is already very popular among Internet and Mobile users, and a recent trend that is gaining acceptance is those based on annotated routes of travel, which we call as trajectories. We are motivated by the need of some users to reveal neither their identity nor location. This is not impossible since exact location can be substituted by an enclosing region, and the identity can be anonymised by relaying all queries through a proxy. However, when users are continuously making queries throughout a session, their queries can contain sufficient correlation which can identify them and/or their queries. Furthermore, a large region will fetch unnecessary search results degrading search quality. This problem of guaranteeing privacy, using smallest possible enclosing regions is NP-hard in general. We propose an efficient greedy algorithm which guarantees a user specified level of location and query privacy, namely k-anonymity and l-diversity, throughout a session and all the while trying to not significantly compromise service quality. Our algorithm, running on the proxy, makes use of trajectories to find similar users whose trajectories are also close by (using appropriate notions of similarity and closeness) for privacy enforcement. We give an indexing structure for efficiently storing and retrieving past trajectories, and present extensive experimental results comparing our approach with other similar approaches.