Privacy-preserving reachability query services for sparse graphs

This paper studies privacy-preserving query services for reachability queries under the paradigm of data outsourcing. Specifically, graph data have been outsourced to a third-party service provider (SP), query clients submit their queries to the SP, and the SP returns the query answers. However, SP may not always be trustworthy. Therefore, this paper considers protecting the structural information of the graph data and the query answers from the SP. This paper proposes simple yet optimized privacy-preserving 2-hop labeling. In particular, this paper proposes that the encrypted intermediate results of encrypted query evaluation are indistinguishable. The proposed technique is secure under chosen plaintext attack. We perform an experimental study on the effectiveness of the proposed techniques on both real-world and synthetic datasets.