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Computers 2017, 6(1), 1; doi:10.3390/computers6010001

BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

1
Department of Computer Science, Comsats Institute of Information Technology, Park Road Chak Shahzad, 44000 Islamabad, Pakistan
2
Laboratoire LINA, Ecole Polytechnique, University of Nantes, 44306 Nantes, France
*
Author to whom correspondence should be addressed.
Academic Editor: Kartik Gopalan
Received: 25 August 2016 / Revised: 16 November 2016 / Accepted: 28 December 2016 / Published: 4 January 2017
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Abstract

Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions. View Full-Text
Keywords: data privacy; generalization; k-anonymity; differential privacy; Bang file data privacy; generalization; k-anonymity; differential privacy; Bang file
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Anjum, A.; Raschia, G. BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication. Computers 2017, 6, 1.

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