Authenticating Multiple User-Defined Spatial Queries

Multiple user decision making is important in today's location-based service scenarios. Existing query services such as kNN and Skyline queries only consider single user and do not consider user's preferences. In this paper, we introduce a novel query type called multiple user-defined spatial queries (MUSQ), which return the best answers for a group of users considering both their locations and preferences. We design an authenticated query processing framework based on MRtree. Comprehensive experiments are conducted to evaluate the proposed methods. The results show the effectiveness and robustness of our methods under various parameter settings.