Towards Practical and Privacy-Preserving Multi-Dimensional Range Query Over Cloud

Abstract

It is undeniable that Internet of Things (IoT) in big data era can provide us with huge volumes of multi-dimensional data, transforming our society into a much more intelligent one. In order to fit for the multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received considerable attention in recent years. However, as the cloud server is not fully trustable, designing multi-dimensional range queries over encrypted data becomes a research trend, and many solutions have been proposed in the literature. Nevertheless, most existing solutions suffer from the leakage of the single-dimensional privacy, and such leakage would severely put the data at risk. Although a few existing works have addressed the problem of single-dimensional privacy, they are impractical in some real scenarios due to the issues of inefficiency, inaccuracy, and two-cloud-server requirement. Aiming at solving these issues, in this article, we propose a practical and privacy-preserving multi-dimensional range query (PRQ) scheme. Specifically, in our proposed PRQ scheme, we first index the multi-dimensional dataset with an R-tree and reduce R-tree based range queries to the problem of point intersection and range intersection. Then, by employing the lightweight matrix encryption technique, we design two novel algorithms for PRQ, i.e., multi-dimensional point intersection predicate encryption (PIPE) and multi-dimensional range intersection predicate encryption (RIPE), which can preserve the privacy of the proposed point intersection algorithm and range intersection algorithm, and further preserve the single-dimensional privacy of the proposed PRQ scheme. Detailed security analysis shows that our proposed PRQ scheme is indeed privacy-preserving. In addition, extensive simulations are conducted, and the results also demonstrate its efficiency.

Topics & Concepts