A Personalizedα,β,l,k-Anonymity Model of Social Network for Protecting Privacy

Abstract

By mining the data published on social network, we can discover the hidden value of information including the privacy of individuals and organizations. Protecting privacy of individuals and organizations on social network has become the focus of more and more researchers. Based on the actual privacy protection need of edge sensitive attribute and vertexes sensitive attribute, we propose a new personalizedα,β,l,k-anonymity technology of privacy preserving to reduce distortion extent of the data in the privacy processing of data of social network. Experimental results of personalizedα,β,l,k-anonymity algorithm show thatd-neighborhood attack of graph, background knowledge attack, and homogeneity attack can be prevented effectively by using anonymous vertexes and edges, as well as the influence matrix based on background knowledge. The diversity of vertex sensitive attribute can be achieved. Personalized protecting privacy requirements can be met by using such parameter asα,β,l,k.