Efficient and generalized geometric range search on encrypted spatial data in the cloud

With cloud services, users can easily host their data in the cloud and retrieve the part needed by search. Searchable encryption is proposed to conduct such process in a privacy-preserving way, which allows a cloud server to perform search over the encrypted data in the cloud according to the search token submitted by the user. However, existing works mainly focus on textual data and merely take numerical spatial data into account. Especially, geometric range search is an important queries on spatial data and has wide applications in machine learning, location-based services(LBS), computer-aided design(CAD), and computational geometry. In this paper, we proposed an efficient and generalized symmetric-key geometric range search scheme on encrypted spatial data in the cloud, which supports queries with different range shapes and dimensions. To provide secure and efficient search, we extend the secure kNN computation with dynamic geometric transformation, which dynamically transforms the points in the dataset and the queried geometric range simultaneously. Besides, we further extend the proposed scheme to support sub-linear search efficiency through novel usage of tree structures. We also present extensive experiments to evaluate the proposed schemes on a real-world dataset. The results show that the proposed schemes are efficient over encrypted datasets and secure against the curious cloud servers.