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BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication
2017
Computers
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
doi:10.3390/computers6010001
fatcat:srokupa4uve2bn25ovys2m3wmq