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Type: Conference paper
Title: Anonymizing hypergraphs with community preservation
Author: Li, Y.
Shen, H.
Citation: Proceedings of the 12th International Conference on Parallell and Distributed Computing, Applications and Technologies, held in Gwangju, South Korea, 20-22 October, 2011: pp.185-190
Publisher: IEEE
Publisher Place: CD
Issue Date: 2011
ISBN: 9781457718076
Conference Name: International Conference on Parallel and Distributed Computing, Applications and Technologies (12th : 2011 : Gwangju, South Korea)
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, with taking community preservation as the objective data utility. The algorithms run in near-quadratic time on hypergraph 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.
Keywords: Identity disclosure
private data publishing
community detection
Rights: © 2011 IEEE
DOI: 10.1109/PDCAT.2011.21
Appears in Collections:Aurora harvest
Computer Science publications

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