Privacy Preserving Moving KNN Queries
  We present a novel approach that protects trajectory privacy of users who access location-based services through a moving k nearest neighbor (MkNN) query. An MkNN query continuously returns the k nearest data objects for a moving user (query point). Simply updating a user's imprecise location such as a region instead of the exact position to a location-based service provider (LSP) cannot ensure privacy of the user for an MkNN query: continuous disclosure of regions enables the LSP to follow a user's trajectory. We identify the problem of trajectory privacy that arises from the overlap of consecutive regions while requesting an MkNN query and provide the first solution to this problem. Our approach allows a user to specify the confidence level that represents a bound of how much more the user may need to travel than the actual kth nearest data object. By hiding a user's required confidence level and the required number of nearest data objects from an LSP, we develop a technique to prevent the LSP from tracking the user's trajectory for MkNN queries. We propose an efficient algorithm for the LSP to find k nearest data objects for a region with a user's specified confidence level, which is an essential component to evaluate an MkNN query in a privacy preserving manner; this algorithm is at least two times faster than the state-of-the-art algorithm. Extensive experimental studies validate the effectiveness of our trajectory privacy protection technique and the efficiency of our algorithm. 
Tanzima Hashem, Lars Kulik, Rui Zhang
cs.DB