A New Multidimensional Data Anonymization Algorithm for Privacy-Preserving Data Publishing

Kara, Burak Cem, Eyupoglu, Can and Karakuş, Oktay ORCID: https://orcid.org/0000-0001-8009-9319 2026. A New Multidimensional Data Anonymization Algorithm for Privacy-Preserving Data Publishing. Electronics 15 (14) , 3167. 10.3390/electronics15143167

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Abstract

Developing a privacy-preserving data publishing algorithm that prevents individuals’ identity information from being disclosed without ignoring the utility of the data is still an important goal to be achieved. Finding the optimal balance between data utility and data privacy is an NP-hard problem. In this study, a new k-anonymization-based data anonymization algorithm is proposed. The proposed algorithm partitions the data space with a new efficient partitioning strategy based on the k-dimensional tree (KD-tree), resolves the boundary problem while maintaining the trade-off balance, and introduces a new mechanism to address the problems caused by outliers. Moreover, it can be applied to both numerical and categorical data. The experimental results indicate that the proposed algorithm achieves competitive or improved performance compared with the baseline algorithms across seven commonly used evaluation metrics. Overall, the findings suggest that the proposed framework can improve the utility–privacy trade-off in multidimensional k-anonymization.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	MDPI
ISSN:	2079-9292
Date of First Compliant Deposit:	28 July 2026
Date of Acceptance:	14 July 2026
Last Modified:	28 Jul 2026 15:30
URI:	https://orca.cardiff.ac.uk/id/eprint/188594

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