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KAB: A new k-anonymity approach based on black hole algorithm

Abstract : 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.
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Contributor : Mahieddine Djoudi Connect in order to contact the contributor
Submitted on : Thursday, September 30, 2021 - 11:41:19 AM
Last modification on : Wednesday, December 22, 2021 - 9:06:02 AM
Long-term archiving on: : Friday, December 31, 2021 - 7:41:27 PM


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Mahieddine Djoudi, Lynda Kacha, Abdelhafid Zitouni. KAB: A new k-anonymity approach based on black hole algorithm. Journal of King Saud University - Computer and Information Sciences, Elsevier 2021, ⟨10.1016/j.jksuci.2021.04.014⟩. ⟨hal-03359517⟩



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