Enhancing utility via clustering in privacy preservation
Yujia Li, Xin Wang, Lei Zhao · 2016
When sensitive information needs to be released involved privacy preservation, which has attracted a widely range of research interests. K-anonymity is one of the most important anonymity models that have been Extensively research and many thinking have been presented to improve it. A way to make data anonymous is generalization. Cluster is a collection of data objects, which are similar to the objects in the same cluster, and different from the objects in other clusters. Both of these two methods have the same characteristics: assign the tuples to many small groups. Based on the above problems, we propose a clustering-based k-anonymity algorithm, which achieves k-anonymity through clustering.