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Type: Journal article
Title: On identity disclosure control for hypergraph-based data publishing
Author: Li, Y.
Shen, H.
Citation: IEEE Transactions on Information Forensics and Security, 2013; 8(8):1384-1396
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2013
ISSN: 1556-6013
Statement of
Yidong Li and Hong Shen
Abstract: Data 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.
Keywords: Anonymization; community detection; hypergraph clustering; identity disclosure; private data publishing
Rights: © 2013 IEEE
RMID: 0020130854
DOI: 10.1109/TIFS.2013.2271425
Appears in Collections:Computer Science publications

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