Anonymizing Hypergraphs with Community Preservation

Data publishing based on hyper graphs is becoming increasingly popular due to its power in representing multi-relations among objects. However, security issues have been little studied on this subject, while most recent work only focuses on the protection of relational data or graphs. As a major privacy breach, identity disclosure reveals the identification of entities with certain background knowledge known by an adversary. In this paper, we first introduce a novel background knowledge attack model based on the property of hyper edge ranks, and formalize the rank-based hyper graph anonymization problem. We then propose a complete solution in a two-step framework, with taking community preservation as the objective data utility. The algorithms run in near-quadratic time on hyper graph size, and protect data from rank attacks with almost same utility preserved. The performances of the methods have been validated by extensive experiments on real-world datasets as well.