EFRS:Enabling Efficient and Fine-Grained Range Search on Encrypted Spatial Data

Range search of spatial data, has been applied in many scenarios such as geometric queries, location-based services, and computational geometry, etc. Due to the increasing amount of spatial data, which are usually outsourced to the cloud for saving storage and computational overhead. However, a common privacy issue is that the cloud server may steal user's sensitive information utilizing its powerful computing advantages. A feasible way of managing this bottleneck is to encrypt spatial data before outsourcing it. Nevertheless, the availability of data will be significantly reduced because of the query difficulty over the encrypted cloud data. In this paper, we propose an Efficient Range Search scheme (EFRS) which can achieve fine- grained query over encrypted spatial data. We original contributions are threefold. First, polynomial fitting technique and orderpreserving encryption are introduced to realize the efficient and fine- grained range query over encrypted cloud data. Then, in order to improve the search efficiency, we exploit the Rtree to significantly decreased the search space. Finally, we theoretically proved the security of our proposed scheme in terms of confidentially of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoor. Besides, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.