Authenticating Preference-Oriented Multiple Users Spatial Queries
Xiaoran Duan, Yong Wang, Juguang Chen, Junhao Zhang · 2017
Location-based social networks (LBSNs) are attracting significant attentions, which make location-aware applications prosperous. We proposed the Multiple User-defined Spatial Query (MUSQ) in [1]. However, it is impractical that non-expert users provide exact vectors to denote their preferences in MUSQ. In this paper, we design a group users weight matrix generation algorithm to represent users' preferences conveniently. In addition, we propose a refinement method to improve the effectiveness of the query results. Further, considering the trust issue introduced by data outsourcing, an authenticated query processing framework is proposed. A set of experiments are conducted to show the effectiveness and scalability of our methods under various parameter settings.