Efficient and secure multi-dimensional geometric range query over encrypted data in cloud

Abstract

Secure geometric range query, which aims to retrieve data points within a given geometric range from an encrypted dataset in the cloud, attracts more and more attention due to its wide applications. Up to now, several secure geometric range query schemes have been put forward. However, the existing schemes still suffer from various disadvantages, such as they are of low efficiency, cannot support multi-dimensional data and general range query, or even have security flaws. In this paper, we study secure geometric range query on encrypted dataset in cloud. First, we show the security problem of the state-of-the-art scheme by proposing an efficient attack method. Then, we propose a new secure solution for general multi-dimensional range query, which is secure under known-background model, and leverage R-tree index to achieve sub-linear search efficiency. Finally, through theoretical analysis and extensive experiments, we demonstrate the effectiveness and efficiency of our proposed approaches.