An Enhanced Personalized Privacy Preserving k-anonymity Model

Recently,anonymization model is one of the hot topic techniques in privacy preserving research.The mainly research is how to avoid leakage of sensitive data in data publishing,but also ensures the efficient use of data.This paper proposed a personalized(αs,l)-diversity k-anonymity model.This method publish the personalized data though generalization technology and α restriction for different code of the generalization tree.This method reserves more information while maintaining the individual privacy.The model are evaluated in an experimental scenario,reserving more information and demonstrating practical applicability of the approaches.