Satisfying link perturbation and k-out anonymous in social network privacy protection

With the development of information technology, social networking has gradually become the main way of communication between people, and sensitive information exposed to the public network led to a variety of privacy issues. In social networks, important nodes have more influence on the virus spread, information dissemination compare to other nodes, therefore, they need more perturbation. In this paper, under the condition of satisfying privacy protection, given the threshold probability of replacing the correct node with a false node; further, we devise algorithms to achieve the graph also satisfied k-out of anonymity, the experimental results demonstrate that our algorithm can preserve the privacy and utility of social networks effectively on real word datasets.