Current developments of k-anonymous data releasing
Jiuyong Li, Hua Wang, Huidong Jin, Jianming Yong · University of Southern Queensland ePrints (University of Southern Queensland) · 2008
Disclosure-control is a traditional statistical methodology for protecting pri-vacy when data is released for analysis. Disclosure-control methods have en-joyed a revival in the data mining community, especially after the introduction of the k-anonymity model by Samarati and Sweeney. Algorithmic advances on k-anonymisation provide simple and effective approaches to protect private in-formation of individuals via only releasing k-anonymous views of a data set. Thus, the k-anonymity model has gained increasing popularity. Recent research identifies some drawbacks of the k-anonymity model and presents enhanced k-anonymity models. This paper reviews problems of the k-anonymity model and its enhanced variants, and different methods for implementing k-anonymity. It compares the k-anonymity model with the secure multiparty computation-based privacy-preserving techniques in the data mining literature. The paper also dis-cusses further development directions of the k-anonymous data releasing. 1