Enabling Efficient and Expressive Spatial Keyword Queries On Encrypted Data

Abstract

Recently, spatial keyword query services have been widely deployed in real-life applications, such as location-based services and social networking. Several privacypreserving spatial keyword queries solutions were proposed to guarantee data security and query privacy on outsourced data. However, those solutions are either based on broken cryptographic tools or support a single query type, and hence cannot meet the security and functionality requirements in practical applications. In this paper, we propose a Secure Spatial Keyword Queries (SSKQ) construction supporting expressive query types. Specifically, we present a secure index structure for spatial-textual data based on the encrypted Quadtree and Bloom filter, which can prune the index tree dynamically and only reveal the files associated with a set of keywords. The security analysis and the experiments conducted on real-world datasets demonstrate the security and performance of our construction.