Loukides, Grigorios  ORCID: https://orcid.org/0000-0003-0888-5061 and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471
2008.
An efficient clustering algorithm for k-anonymisation.
Journal of Computer Science and Technology
23
(2)
, pp. 188-202.
10.1007/s11390-008-9121-3
|
Abstract
K-anonymisation is an approach to protecting individuals from being identified from data. Good k-anonymisations should retain data utility and preserve privacy, but few methods have considered these two con°icting requirements together. In this paper, we extend our previous work on a clustering-based method for balancing data utility and privacy protection, and propose a set of heuristics to improve its effectiveness. We introduce new clustering criteria that treat utility and privacy on equal terms and propose sampling-based techniques to optimally set up its parameters. Extensive experiments show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Uncontrolled Keywords: | k-anonymisation - data privacy - greedy clustering |
| Publisher: | Springer |
| ISSN: | 1000-9000 |
| Last Modified: | 18 Oct 2022 13:32 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/14242 |
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