Abstract. 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.
Hui et al. REF proposed an enhanced privacy protection model and implemented the algorithm, combining the advantages of k-anonymous model and l-diversity model.
An Improved Anonymous Privacy Protection Model
DEStech Transactions on Computer Science and Engineering
Computer Science