Journal
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
Volume 34, Issue 7, Pages 4075-4088Publisher
ELSEVIER
DOI: 10.1016/j.jksuci.2021.04.014
Keywords
Privacy; Anonymization; K-anonymity; Clustering; Blackholealgorithm
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K-anonymity is a widely used approach for privacy preservation in microdata, but it suffers from information loss. To address this issue, this paper proposes a novel algorithm based on the Black Hole Algorithm, which improves data utility.
K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. Although generalization-based k-anonymity approaches can achieve the privacy protection objective, they suffer from information loss. Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. We propose, in this paper, a novel algorithm based on a simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), to address such limitations. Experiments on real data set show that data utility has been improved by our approach compared to k-anonymity, BHA-based k-anonymity and clustering-based k-anonymity approaches. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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