Priority driven K-anonymisation for privacy protection

Sun, Xiaoxun and Wang, Hua and Li, Jiuyong (2008) Priority driven K-anonymisation for privacy protection. In: 7th Australasian Data Mining Conference (AusDM 2008), 27-28 Nov 2008, Glenelg, Adelaide.

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Abstract

[Abstract]: Given the threat of re-identi¯cation in our growing digital society, guaranteeing privacy while providing worthwhile data for knowledge discovery has become a diffcult problem. K-anonymity is a major technique used to ensure privacy by generalizing and suppressing attributes and has been the focus of intense research in the last few years. However, data modification techniques like generalization may produce anonymous data unusable for medical studies because some attributes become too coarse-grained. In this paper, we propose a priority driven k-anonymisation that allows to specify the degree of acceptable distortion for each attribute separately. We also defined some appropriate metrics to measure the distance and information loss, which are suitable for both numerical and categorical attributes. Further, we formulate the priority driven k-anonymisation as the k-nearest neighbor (KNN) clustering problem by adding a con- straint that each cluster contains at least k tuples. We develop an efficient algorithm for priority driven k-anonymisation. Experimental results show that the proposed technique causes significantly less distor- tions.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher. Copyright ©2008, Australian Computer Society, Inc. This paper appeared at the Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, Australia. Con- ferences in Research and Practice in Information Technology, Vol. 87. John F. Roddick, Jiuyong Li, Peter Christen and Paul Kennedy, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.
Depositing User: Dr Hua Wang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 19 Oct 2009 23:55
Last Modified: 14 Oct 2011 04:25
Uncontrolled Keywords: K-anonymity; privacy protection
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080204 Mathematical Software
URI: http://eprints.usq.edu.au/id/eprint/5901

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