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},
websites = {https://www.sciencedirect.com/science/article/pii/S1319157821001002},
id = {7d2d84b4-b3a1-3202-94d0-61fe276d7582},
created = {2021-09-30T05:23:25.052Z},
file_attached = {false},
profile_id = {3f3cebd9-2c9e-33e2-9759-3b3c3deedc23},
last_modified = {2021-09-30T05:23:25.052Z},
read = {false},
starred = {false},
authored = {true},
confirmed = {true},
hidden = {false},
citation_key = {KACHA2021},
source_type = {article},
private_publication = {false},
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}
}
Downloads: 0
{"_id":"2uYJwaFqQAgqTngso","bibbaseid":"kacha-zitouni-djoudi-kabanewkanonymityapproachbasedonblackholealgorithm-2021","author_short":["Kacha, L.","Zitouni, A.","Djoudi, M."],"bibdata":{"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","websites":"https://www.sciencedirect.com/science/article/pii/S1319157821001002","id":"7d2d84b4-b3a1-3202-94d0-61fe276d7582","created":"2021-09-30T05:23:25.052Z","file_attached":false,"profile_id":"3f3cebd9-2c9e-33e2-9759-3b3c3deedc23","last_modified":"2021-09-30T05:23:25.052Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"KACHA2021","source_type":"article","private_publication":false,"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","bibtex":"@article{\n title = {KAB: A new k-anonymity approach based on black hole algorithm},\n type = {article},\n year = {2021},\n keywords = {Anonymization,Black hole algorithm,Clustering,K-anonymity,Privacy},\n websites = {https://www.sciencedirect.com/science/article/pii/S1319157821001002},\n id = {7d2d84b4-b3a1-3202-94d0-61fe276d7582},\n created = {2021-09-30T05:23:25.052Z},\n file_attached = {false},\n profile_id = {3f3cebd9-2c9e-33e2-9759-3b3c3deedc23},\n last_modified = {2021-09-30T05:23:25.052Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {KACHA2021},\n source_type = {article},\n private_publication = {false},\n 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.},\n bibtype = {article},\n author = {Kacha, Lynda and Zitouni, Abdelhafid and Djoudi, Mahieddine},\n doi = {https://doi.org/10.1016/j.jksuci.2021.04.014},\n journal = {Journal of King Saud University - Computer and Information Sciences}\n}","author_short":["Kacha, L.","Zitouni, A.","Djoudi, M."],"urls":{"Website":"https://www.sciencedirect.com/science/article/pii/S1319157821001002"},"biburl":"https://bibbase.org/service/mendeley/3f3cebd9-2c9e-33e2-9759-3b3c3deedc23","bibbaseid":"kacha-zitouni-djoudi-kabanewkanonymityapproachbasedonblackholealgorithm-2021","role":"author","keyword":["Anonymization","Black hole algorithm","Clustering","K-anonymity","Privacy"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/3f3cebd9-2c9e-33e2-9759-3b3c3deedc23","dataSources":["rh94BkjSH73SyjZsR","ya2CyA73rpZseyrZ8","B9CSJFaMFh3P3584A","G2mFzxcNhwvPwybbi","PgsKQnKXuGehwfH4n","GLMs4nYirTtANpSJL"],"keywords":["anonymization","black hole algorithm","clustering","k-anonymity","privacy"],"search_terms":["kab","new","anonymity","approach","based","black","hole","algorithm","kacha","zitouni","djoudi"],"title":"KAB: A new k-anonymity approach based on black hole algorithm","year":2021}