Privacy-preserving Spatio-Textual Skylines Based on Location Aggregation

To achieve cost-savings, data owners outsource their spatio-textual query services to public cloud, which, however, may bring serious privacy issues. In this paper, we define and study the problem of privacy-preserving spatio-textual skylines in cloud environments. To address the problem, we first transform the locations and texts of each data object and query request into vectors and encrypt the vectors based on an vector-based encryption method to protect the data privacy. Exploiting the group of query locations to accelerate the query processing, we further present a location-aggregation-based query request generation method. Based on encrypted aggregated query requests, we present a corresponding query processing algorithm for privacy-preserving spatio-textual skylines. Finally, we present analysis to show the security guarantee of the proposed methods, and conduct thorough experiments on real datasets to show the performance of our algorithms.