Privacy Preserving Strong Simulation Queries on Large Graphs

Abstract

This paper studies privacy preserving query services for strong simulation queries in the database outsourcing paradigm. In such a paradigm, clients send their queries to a third-party service provider (SP), who has the outsourced large graph data, and the SP computes the query answers. However, as SP may not always be trusted, the sensitive information of the clients’ queries, importantly, the query structures, should be protected. Moreover, graph pattern queries often have high complexities, whereas data graphs can be large. This paper adopts strong simulation as a practical query semantic for this paradigm. Under this semantic, queries are matched with a notion of balls, which are subgraphs related to the query diameter. We transform the core of the existing strong simulation algorithm using data-oblivious operations (ObSSA) and propose its secure version. We show that the algorithm may encounter an overflow problem even partially homomorphic encryption (PHE) has been used. We then propose an efficient inexact algorithm EncSSA, which is secure under chosen plaintext attack (CPA). The results of privacy analysis are presented. We have conducted experiments on Twitter and Citeseer datasets, and the results show that EncSSA is both efficient and effective.