Preventing range disclosure in k-anonymised data

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2011. Preventing range disclosure in k-anonymised data. Expert Systems with Applications 38 (4) , pp. 4559-4574. 10.1016/j.eswa.2010.09.130

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Abstract

k-Anonymisation is an approach to preventing sensitive information about individuals being identified or inferred from a dataset. Existing work achieves this by ensuring that each individual is linked to multiple sensitive values, but they have not adequately considered how the range formed by these sensitive values may affect privacy protection. When such a range is small, sensitive information about individuals may still be inferred quite accurately, thereby breaching privacy. In this paper, we study the problem of range disclosure (i.e. estimating sensitive information through ranges) in k-anonymisation, and propose Range Diversity for quantifying the effect of range disclosure on privacy protection. Our measure considers several possible attacks and allows anonymisers to specify the level of protection required in a flexible manner. Extensive experiments show that range diversity provides better protection for range disclosure and higher level of data utility than the existing methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Uncontrolled Keywords:	Data privacy; Range disclosure; k-Anonymisation
Publisher:	Elsevier
ISSN:	0957-4174
Last Modified:	18 Oct 2022 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/14159

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