On identity disclosure control for hypergraph-based data publishing

dc.contributor.authorLi, Y.
dc.contributor.authorShen, H.
dc.date.issued2013
dc.description.abstractData publishing based on hypergraphs is becoming increasingly popular due to its power in representing multirelations 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 hyperedge ranks, and formalize the rank-based hypergraph anonymization problem. We then propose a complete solution in a two-step framework: rank anonymization and hypergraph reconstruction. We also take hypergraph 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 hypergraph size, and protect data from rank attacks with almost the 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 hypergraph reconstruction are, to our best knowledge, the first systematic study to privacy preserving for hypergraph-based data publishing.
dc.description.statementofresponsibilityYidong Li and Hong Shen
dc.identifier.citationIEEE Transactions on Information Forensics and Security, 2013; 8(8):1384-1396
dc.identifier.doi10.1109/TIFS.2013.2271425
dc.identifier.issn1556-6013
dc.identifier.issn1556-6021
dc.identifier.orcidShen, H. [0000-0002-3663-6591] [0000-0003-0649-0648]
dc.identifier.urihttp://hdl.handle.net/2440/79987
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.rights© 2013 IEEE
dc.source.urihttps://doi.org/10.1109/tifs.2013.2271425
dc.subjectAnonymization; community detection; hypergraph clustering; identity disclosure; private data publishing
dc.titleOn identity disclosure control for hypergraph-based data publishing
dc.typeJournal article
pubs.publication-statusPublished

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