Preventing identity disclosure in hypergraphs

Yidong Li, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data publishing based on hypergraphs 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 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 construction. 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 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 construction are, to our best knowledge, the first systematic study to privacy preserving for hypergraph-based data publishing.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages659-665
Number of pages7
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: 11 Dec 201111 Dec 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Country/TerritoryCanada
CityVancouver, BC
Period11/12/1111/12/11

Keywords

  • Anonymization
  • Community detection
  • Hypergraph clustering
  • Identity disclosure
  • Private data publishing

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