An efficient big data anonymization algorithm based on chaos and perturbation techniques

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dc.contributor.authorEyüpoğlu, Can
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorZaim, Abdül Halim
dc.contributor.authorSertbaş, Ahmet
dc.date.accessioned2020-11-21T15:53:48Z
dc.date.available2020-11-21T15:53:48Z
dc.date.issued2018en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThe topic of big data has attracted increasing interest in recent years. The emergence of big data leads to new difficulties in terms of protection models used for data privacy, which is of necessity for sharing and processing data. Protecting individuals' sensitive information while maintaining the usability of the data set published is the most important challenge in privacy preserving. In this regard, data anonymization methods are utilized in order to protect data against identity disclosure and linking attacks. In this study, a novel data anonymization algorithm based on chaos and perturbation has been proposed for privacy and utility preserving in big data. The performance of the proposed algorithm is evaluated in terms of Kullback-Leibler divergence, probabilistic anonymity, classification accuracy, F-measure and execution time. The experimental results have shown that the proposed algorithm is efficient and performs better in terms of Kullback-Leibler divergence, classification accuracy and F-measure compared to most of the existing algorithms using the same data set. Resulting from applying chaos to perturb data, such successful algorithm is promising to be used in privacy preserving data mining and data publishing. © 2018 by the authors.en_US
dc.identifier.doi10.3390/e20050373en_US
dc.identifier.issn1099-4300
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85057860878en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/e20050373
dc.identifier.urihttps://hdl.handle.net/11467/3686
dc.identifier.volume20en_US
dc.identifier.wosWOS:000435193100066en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofEntropyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBig dataen_US
dc.subjectChaosen_US
dc.subjectData anonymizationen_US
dc.subjectData perturbationen_US
dc.subjectPrivacy preservingen_US
dc.titleAn efficient big data anonymization algorithm based on chaos and perturbation techniquesen_US
dc.typeArticleen_US

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