KAB: A new k-anonymity approach based on black hole algorithm. Kacha, L., Zitouni, A., & Djoudi, M. Journal of King Saud University - Computer and Information Sciences, 2021. ![KAB: A new k-anonymity approach based on black hole algorithm [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 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.
@article{
title = {KAB: A new k-anonymity approach based on black hole algorithm},
type = {article},
year = {2021},
keywords = {Anonymization,Black hole algorithm,Clustering,K-anonymity,Privacy},
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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.},
bibtype = {article},
author = {Kacha, Lynda and Zitouni, Abdelhafid and Djoudi, Mahieddine},
doi = {https://doi.org/10.1016/j.jksuci.2021.04.014},
journal = {Journal of King Saud University - Computer and Information Sciences}
}
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