Efficient privacy-preserving circular range search on outsourced spatial data

With the growing popularity of outsourcing data and services to the cloud, performing queries on encrypted data becomes a promising technique. Searchable encryption (SE) allows encryption while still enabling search for a variety of data. However, most of the existing arts focus on rectangular range query on common database. Query on encrypted spatial database has not been well studied. Moreover, as a vital type of geometric query on spatial data, the circular range search (CRS) is widely utilized in Location-Based Services (LBSs) and computational geometry. A recently proposed CRS scheme achieved security and privacy requirements. However, it exhibits low performance in terms of encryption and search efficiency. In this paper, we propose an Efficient Privacy-preserving CRS scheme (EP-CRS) on outsourced spatial data. Specifically, our scheme achieves CRS by leveraging an R-tree based SE scheme and adding a trusted-third party (TTP) to system. Security analysis indicates that EP-CRS can preserve data and query privacy. In addition, we conduct real experiments and compare EP-CRS with the existing one to show that the proposal is more efficient in terms of data encryption, token generation and search.