Next Article in Journal
Acknowledgement to Reviewers of Computers in 2016
Previous Article in Journal
BSEA: A Blind Sealed-Bid E-Auction Scheme for E-Commerce Applications
Article Menu

Export Article

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

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

Department of Computer Science, Comsats Institute of Information Technology, Park Road Chak Shahzad, 44000 Islamabad, Pakistan
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
View Full-Text   |   Download PDF [1596 KB, uploaded 4 January 2017]   |  


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

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top