BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication
AbstractPrivacy-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
Scifeed alert for new publicationsNever 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
Anjum, A.; Raschia, G. BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication. Computers 2017, 6, 1.
Anjum A, Raschia G. BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication. Computers. 2017; 6(1):1.Chicago/Turabian Style
Anjum, Adeel; Raschia, Guillaume. 2017. "BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication." Computers 6, no. 1: 1.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.