An Uncertain Graph Approach for Preserving Privacy in Social Networks Based on Important Nodes

With the widespread popularization of Social network platforms, social network has gradually become a part of people's life, which contains not only large valuable data for researching but also privacy information. Unfortunately, analysis of the data of social networks can disclose individual privacy. Thus, it is quite essential to take privacy preserving before the data is released. To overcome this problem, we put forward a novel uncertain graph approach, which balances the relationship between privacy and utility. Because the important nodes in social network are concerned by the attackers, We reach the goal of privacy preserving for the network by modifying the structure of important nodes. Different from other uncertain graph approaches, This approach not only achieves the purpose of privacy preserving, but also effectively maintains the structure of the network. After a large number of experiments, the results showed that our approach has more advantages over other existing uncertain approaches in privacy preserving and data utility of social network.