Achieving Privacy-preserving Arbitrary Geometric Range Query with Bilateral Access Control

Recently, the cloud server has been extensively applied to store large amounts of location data and provide geometric range query services. However, this may pose severe privacy concerns. Existing privacy-preserving geometric range query schemes either rely on high-cost encryption techniques or cannot support arbitrary geometric range query. Moreover, in these schemes, location data can be queried and accessed by some unauthorized data requesters, and the data requester may acquire location data from some unauthorized data owners. The former will compromise the privacy of location data, and the latter will bring non-compliant data to the data requester. In this paper, we design an arbitrary geometric range query scheme that enables efficient and privacy-preserving querying with bilateral access control. Specifically, we use polynomial curves to fit arbitrary geometric ranges and extract the polynomial coefficients as query trapdoors. We employ random matrices to protect the privacy of location data, query trapdoors, and access policies. Then, due to the properties of random matrix multiplication, the cloud server can securely query encrypted location data with bilateral access control. Extensive experiments indicate that our scheme outperforms state-of-the-art work with respect to computational and communication overheads.