Data-part: A technique for privacy protection in databases
Anand Kumar Gupta, Atul Sunsunwal, Swati Singhal · 2008
In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. Datafly is one such technique which generalizes the information up to a level in such a way that each individual is hidden among at least k-1 other individuals. But this technique sometimes over distorts the data which may render it useless for statistical and research purposes. In this paper, we present a method called ldquoData-Partrdquo, based on ldquoDataflyrdquo, but with certain modifications, which tries to minimize the distortion of data while still maintaining adequate privacy protection. We prove it through results and experiments at the end of this paper.