Preventing Identity Disclosure in Hypergraphs

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: rank anonymization and hyper graph construction. We also take hyper graph clustering (known as community detection) as data utility into consideration, and discuss two metrics to quantify information loss incurred in the perturbation. Our approaches are effective in terms of efficacy, privacy and 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. Our rank-based attack model and algorithms for rank anonymization and hyper graph construction are, to our best knowledge, the first systematic study to privacy preserving for based-based data publishing.