Current developments of k-anonymous data releasing

Disclosure-control is a traditional statistical methodology for protecting privacy when data is released for analysis. Disclosure-control methods have enjoyed 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 information 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 kanonymity 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 discusses further development directions of the k-anonymous data releasing.

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