Achieving Efficient and Privacy-Preserving Reverse Skyline Query Over Single Cloud

Abstract

Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine whether the inner product of two vectors satisfies a specific relation with 0 without leaking the vectors’ information. Next, we propose a privacy-preserving reverse dominance checking scheme (PRDC) based on symmetric homomorphic encryption. Finally, we achieve ePRSQ based on PIPSD and PRDC. Security analysis shows that PIPSD and PRDC are both secure in the real/ideal world model, and ePRSQ can protect the security of the dataset, the privacy of query requests and query results. Extensive experiments show that ePRSQ is efficient. Specifically, for a 3-dimensional dataset of size 1000, the computational and communication overheads of ePRSQ for a query are 79.47 s and 0.0021 MB, respectively. The efficiency is improved by $3.78\times$ (300.58 s) and $928.57\times$ (1.95 MB) respectively compared with PPARS, and by $61.31\times$ (4872.55 s) and $407309\times$ (855.35 MB) respectively compared with OPPRS.