A privacy-aware monitoring algorithm for moving k-nearest neighbor queries in road networks

Location privacy is a major obstacle in the ubiquitous deployment of mobile and pervasive computing services. In this study, we present a new approach for preserving the trajectory privacy of moving $$k$$k-nearest neighbor (M$$k$$kNN) queries in road networks. Several location anonymization algorithms have been proposed for providing location privacy to users traveling on a road network. These algorithms focus primarily on the location anonymization of snapshot queries. Indeed, users move freely and arbitrarily, and thus query results provided to them soon become invalid as their locations change. To refresh the query result, each user must therefore periodically contact the location-based service, enabling attackers to identify and track the user easily. In addition, frequent location updates for the user may incur severe computational and communication costs. We address these issues by proposing a privacy-aware monitoring algorithm, called PAMA, for preserving the trajectory privacy of M$$k$$kNN queries in road networks. Our simulation results show that PAMA significantly outperforms conventional algorithms in terms of both security and performance.