Countering overlapping rectangle privacy attack for moving kNN queries
An important class of LBSs is supported by the moving k nearest neighbor (MkNN) query, which continuously returns the k nearest data objects for a moving user. For example, a tourist may want to observe the five nearest restaurants continuously while exploring a city so that she can drop in to one of them anytime. Using this kind of services requires the user to disclose her location continuously and therefore may cause privacy leaks derived from the user's locations. A common approach to protecting a user's location privacy is the use of imprecise locations (e.g., regions) instead of exact positions when requesting LBSs. However, simply updating a user's imprecise location to a location-based service provider (LSP) cannot ensure a user's privacy for an MkNN query: continuous disclosure of regions enable LSPs to refine more precise location of the user. We formulate this type of attack to a user's location privacy that arises from overlapping consecutive regions, and provide the first solution to counter this attack. Specifically, we develop algorithms which can process an MkNN query while protecting the user's privacy from the above attack. Extensive experiments validate the effectiveness of our privacy protection technique and the efficiency of our algorithm.
moving knn queries location privacy confidence level overlapping rectangle attack