Greedy clustering with sample-based heuristics for k-anonymisation

Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2007. Greedy clustering with sample-based heuristics for k-anonymisation. Presented at: The First International Symposium on Data, Privacy, and E-Commerce, 2007, Chengdu, Sichuan, 1-3 November 2007. Proceedings of the First International Symposium on Data, Privacy, and E-Commerce, 2007. Los Alamitos, CA: IEEE, pp. 191-196. 10.1109/ISDPE.2007.102

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Abstract

Developing techniques for k-anonymising data has received much recent attention from the database research community. Good k-anonymisations should retain data utility and preserve privacy, but these are conflicting requirements and can only be traded-off. A method proposed recently attempted to achieve a balance between these two requirements, but its efficiency and effectiveness depend heavily on several empirically set parameters. In this paper, we propose sampling-based heuristics to optimally set up these parameters. We test the effectiveness of our methods by evaluating anonymisations in terms of accuracy in query answering and ability to prevent linking attacks.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	data k-anonymisation, data privacy preservation, data utility, database research, greedy clustering, linking attacks, query answering, sample-based heuristics, sampling-based heuristics
Publisher:	IEEE
ISBN:	9780769530161
Last Modified:	24 Oct 2022 10:37
URI:	https://orca.cardiff.ac.uk/id/eprint/45111

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