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Article

An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques

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Department of Computer Engineering, Istanbul Commerce University, Istanbul 34840, Turkey
2
Department of Computer Engineering, Istanbul University, Istanbul 34320, Turkey
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Author to whom correspondence should be addressed.
This work is a part of the Ph.D. thesis titled “Software Design for Efficient Privacy Preserving in Big Data” at Institute of Graduate Studies in Science and Engineering, Istanbul University, Istanbul, Turkey.
Entropy 2018, 20(5), 373; https://doi.org/10.3390/e20050373
Received: 21 April 2018 / Revised: 12 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
(This article belongs to the Section Information Theory, Probability and Statistics)
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. View Full-Text
Keywords: big data; chaos; data anonymization; data perturbation; privacy preserving big data; chaos; data anonymization; data perturbation; privacy preserving
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MDPI and ACS Style

Eyupoglu, C.; Aydin, M.A.; Zaim, A.H.; Sertbas, A. An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques. Entropy 2018, 20, 373. https://doi.org/10.3390/e20050373

AMA Style

Eyupoglu C, Aydin MA, Zaim AH, Sertbas A. An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques. Entropy. 2018; 20(5):373. https://doi.org/10.3390/e20050373

Chicago/Turabian Style

Eyupoglu, Can, Muhammed A. Aydin, Abdul H. Zaim, and Ahmet Sertbas. 2018. "An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques" Entropy 20, no. 5: 373. https://doi.org/10.3390/e20050373

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