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

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Tarih

2018

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

MDPI AG

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

The 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.

Açıklama

Anahtar Kelimeler

Big data, Chaos, Data anonymization, Data perturbation, Privacy preserving

Kaynak

Entropy

WoS Q Değeri

Q2

Scopus Q Değeri

Q2

Cilt

20

Sayı

5

Künye