Pystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top-$k$ Spatial–Textual Query

Abstract

The convergence of technologies like Cloud computing, mobile, and smart phone technologies has led to the rapid development of location-based services (LBS) in smart cities. For flexibility and cost savings, there is a recent trend to migrate LBS to the Cloud, however it poses a serious threat to the user privacy. In this paper, we present a new privacy preserving top- $k$ spatio-textual keyword ( $\text{T}{k}$ SK) query scheme, called privacy-preserving spatio-textual index (Pystin), which is performed over outsourced Cloud and can enable secure LBS in smart cities. In Pystin, a query user’s accurate location is protected by the combination of Boneh–Goh–Nissim homomorphic encryption and hash bucket techniques, and the privacy of textual information are persevered by a one-way hash function. In addition, a quad-tree-based spatio-textual indexing is integrated into Pystin to further reduce the query latency. Detailed security analyzes show that the proposed Pystin scheme is indeed a privacy-preserving $\text{T}{k}$ SK query scheme. Furthermore, extensive experiments are conducted, and results confirm the scalability, efficiency properties of our proposed Pystin scheme.