With the advances in wireless Internet and mobile positioning technology, location-based services (LBSs) have become popular. In LBSs, users must send their exact locations in order to use the services, but they may be subject to several privacy threats. To solve this problem, query processing algorithms based on a cloaking method have been proposed. The algorithms use spatial cloaking methods to blur the user's exact location in a region satisfying the required privacy threshold (k). With the cloaked region, an LBS server can execute a spatial query processing algorithm preserving their privacy. However, the existing algorithms cannot provide good query processing performance. To resolve this problem, we, in this paper, propose a k-NN query processing algorithm based on network Voronoi diagram for spatial networks. Therefore, our algorithm can reduce network expansion overhead and share the information of the expanded road network. In order to demonstrate the efficiency of our algorithms, we have conducted extensive performance evaluations. The results show that our algorithm achieves better performance on retrieval time than the existing algorithms, such as PSNN and kRNN. This is because our k-NN query processing algorithm can greatly reduce a network expansion cost for retrieving k POIs.

Even though it is very important to retrieve similar trajectories with a given query trajectory, there has been a little research on trajectory retrieval in spatial networks, like road networks. In this paper, we propose an efficient indexing scheme for retrieving moving object trajectories in spatial networks. For this, we design a signature-based indexing scheme for efficiently dealing with the trajectories of current moving objects as well as for maintaining those of past moving objects. In addition, we provide an insertion algorithm for storing the segment information of a moving object trajectory as well as a retrieval algorithm to find a set of moving objects whose trajectories match the segments of a query trajectory. Finally, we show that our signature-based indexing scheme achieves at least twice better performance on trajectory retrieval than the leading trajectory indexing schemes, such as TB-tree, FNR-tree, and MON-tree.