SPCS: Strong Privacy-Preserving-Constrained Shortest Distance Queries on Encrypted Graphs
Wenli Wang, Zhuliang Jia, Mengfan Xu, Shundong Li
Abstract
A constrained shortest distance (CSD) query calculates the shortest distance between two vertices of a graph and places constraints on certain factors so as not to exceed corresponding thresholds. With the development of cloud computing, outsourcing graph data storage and the computation of CSD queries to a cloud platform (CP) are an attractive choice for graph owners; however, this choice is accompanied by well-known privacy issues. Certain privacy-preserving schemes have been proposed to support CSD queries on encrypted graphs, but all such schemes consider only single-CSD queries, even though, in practical applications, users may need to set two or more constraints when executing the shortest distance query. Additionally, existing schemes disclose too much private information to the CP. To address these considerable problems, we propose a strong privacy-preserving CSD query scheme called SPCS, which realizes accurate double-CSD queries without disclosing any critical private information. We prove that SPCS does not reveal any private information to the CP except for the number of vertices and conduct numerous experiments on real-world data sets to test this scheme’s efficiency. The experimental results show that SPCS is practical, especially in its computational efficiency in the graph-encryption phase, which is higher than that of available, state-of-the-art schemes.