Secure Spatio-textual Skyline Queries on Cloud Platform
Yiping Teng, Dan Liu, Xiaoting Liu, Weiyu Zhao, Haigang Liu, Chunlong Fan · 2020
With the merging of geographical locations and textual descriptions in mobile Internet, processing spatio-textual skyline queries to retrieve points of interest based on relevance of spatiality and textuality has been commonly utilized in LBS applications. With cost-savings and flexibility, outsourcing data retrievals motivates data owners to provide their services through public clouds. However, it may cause serious privacy concerns. In this paper, we define and study the problem of secure spatio-textual skyline query processing in cloud environments. To tackle this problem, we propose two secure spatio-textual skyline query approaches. In the basic approach, with encrypted objects and query requests, a secure query is processed via a linear scanning manner assisted by secure spatio-textual dominance computation over objects. To improve the efficiency of the basic approach, we further propose an index-based approach, in which we employ a secure tree-based index. To retrieve the secure index, we facilitate secure spatio-textual skyline queries by devising secure spatio-textual dominance computation over encrypted tree nodes. The security guarantees of our approaches are theoretically analyzed, and the query performance on real datasets is shown in experimental results.