Clustering-based approach for multi-level anonymization

Abstract

To prevent the privacy disclosure caused by linking attack and reduce information loss resulting from anonymous protection,a(λα,k) multi-level anonymity model was proposed.According to the requirement of privacy preservation,sensitive attribute values could be divided into three levels:high,medium,and low.The risk of privacy disclosure was flexibly controlled by privacy protection degree parameter λ.On the basis of this,clustering-based approach for multi-level anonymization was proposed.The approach used a new hierarchical clustering algorithm and adopted more flexible strategies of data generalization for numerical attributes and classified attributes in a quasi-identifier.The experimental results show that the approach can meet the requirement of multi-level anonymous protection of sensitive attribute,and effectively reduce information loss.