Enabling Efficient And Expressive Spatial Keyword Queries On Encrypted Data

Recently, spatial keyword query services have been widely deployed in real-life applications, such as location-based services and social networking. Several privacy-preserving 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 \textbf{S}ecure \textbf{S}patial \textbf{K}eyword \textbf{Q}ueries (SSKQ) construction supporting expressive query types. Specifically, we present a secure index structure for \textit{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.