k-Anonymity via Twice Clustering for Privacy Preservation
Min Zhou · Journal of Jilin University · 2009
k-anonymity is a current hot spot for privacy preservation.The existing k-anonymous methods ignored the quasi-identifier's different influences on the sensitive attributes and clustered the tuples only,which caused much information loss while publishing the data.To cope with this problem,a novel k-anonymity via twice clustering and the concept of influence matrix to express the quasi-identifier's influences on different sensitive attributes are proposed.The clustering techniques over influence matrix are investigated and the tuples with near influences on the sensitive attributes are clustered to achieve k-anonymity.The experimental results show that the proposed methods are effective and feasible to privacy preservation.Compared with basic k-anonymity,the methods have less average equivalence class size and less run time.