| Título: | Current Developments of k-Anonymous Data Releasing |
| Autores: |
Li, Jiuyong; School of Computer and Information Science, University of South Australia, Adelaide Wang, Hua; Department of Mathematics and Computing, University of Southern Queensland, Toowoomba Jin, Huidong; NICTA Canberra Lab, Canberra ACT, Australia Yong, Jianming; School of Information Systems, University of Southern Queensland, Toowoomba |
| Fecha: | 2008-03-31 |
| Publicador: | Electronic journal of health informatics |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: |
Privacy Preserving; Data Releasing; k-Anonymity Privacy Preserving; Data Releasing; k-Anonymity |
| Descripción: | 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 kanonymity 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 discusses further development directions of the k-anonymous data releasing. |
| Idioma: | Inglés |
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