Social Privacy-Preserving Modeling Based on Graphical Evolutionary Game and Infectious Disease Dissemination Dynamics

Although social network provides more convenience for people's daily communication, it also faces the risk of personal privacy leakage. How to effectively evaluate and protect personal privacy has become a challenging issue in social networks. In this article, we propose a risk assessment-based privacy-preserving model for social networks, which is based on graphical evolutionary game theory and infectious disease dissemination dynamics model. In our scheme, a privacy risk assessment model is constructed to evaluate the importance degree of users’ privacy information and measure the leakage risk of users’ privacy information. Second, due to the heterogeneity of social networks, a graphical evolutionary game model of user privacy information forwarding behaviors is built to quantify and measure the strategy (or behavior) changes of different social users. Further, we construct a privacy-preserving information dissemination model, which is based on infectious disease dissemination model combined with graphical evolutionary game theory. Our risk assessment-based privacy-preserving model is to accurately characterize the dissemination of privacy information and reduce the transmission of privacy information, so as to protect social users’ privacy information. Related experimental results show the effectiveness of our privacy-preserving model by adjusting the payoff of social users.