An Improved Anonymous Privacy Protection Model
Hui Jong Yu, Ming-Gao SHE · DEStech Transactions on Computer Science and Engineering · 2017
Aiming at the privacy protection problem in the current data release, the classical K-Anonymity model and the improved L-Diversity model are analyzed. Combining the advantages of the two models, an enhanced privacy protection model is proposed and the algorithm is implemented. The new model enhances the validity of data distribution by introducing clustering method. At the same time in the clustering process using a new information loss measurement standards to enhance the security and flexibility of data release. The experimental results show that the model can reduce the risk of privacy leakage, and has a small loss of information.