Performance Enhanced Secure Spatial Keyword Similarity Query with Arbitrary Spatial Ranges

The increasing prevalence of cloud computing drives the exploration of various secure query schemes over encrypted data, among which secure spatial keyword query has drawn a great deal of attention due to its broad application in location-based services. However, most existing schemes are either limited to the boolean keyword test or incapable of protecting access pattern privacy. Although the state-of-the-art secure spatial keyword query scheme can support keyword similarity while preserving access pattern privacy, it is unable to cope with the arbitrary spatial range, which is more general, and has limitations in efficiency and security. In this paper, we propose a new secure spatial keyword similarity query scheme that can support arbitrary spatial ranges and enhance the efficiency and security of the state-of-the-art scheme at the same time. Specifically, we first present a new homomorphic encryption technique by improving the popular symmetric homomorphic encryption (SHE). After that, we propose a novel approach to make supporting arbitrary spatial ranges over encrypted data possible, in which a spatial encoding technique is designed to improve performance. Finally, by designing a pack-based solution to protect access pattern privacy, our proposed scheme can hide the number of query results while optimizing performance. We formally prove the security of our proposed scheme and conduct experiments to evaluate its performance. The results indicate that our proposed scheme outperforms the state-of-the-art scheme in both the computational costs and communication overhead.