Evolutionary Game Based Analysis for User Privacy Protection Behaviors in Social Networks

Fang Liu, Li Pan, Lihong Yao

Abstract: With the increasing development of online social networks, privacy protection attracts more and more concerns. Most research on privacy protection focus on specific techniques, and they assume users adopt security protection mechanisms for granted. The impact of user behaviors on the deployment of these mechanisms is usually neglected. In fact, the actual effect of privacy protection mechanisms depends heavily on user behaviors. Whether a user adopts the security mechanism is affected by incentives, interactions with friends and other factors. It is necessary to encourage users to take privacy protection settings in order to improve the security environment in social networks. Therefore, in this paper, a network evolutionary game model is proposed for studying the influence of user behaviors on the deployment of privacy protection mechanisms, where whether to take privacy settings is used as game strategies. Considering real interactions in social networks, games only happen between connected users in the model. Then the dynamic equation of user behaviors over time is derived. Based on the dynamic equation, evolutionary stability conditions are proven according to the benefit cost ratio of taking the protection. Experiments in real social networks verify the feasibility of the proposed model. The analysis results are helpful for guiding security services to promote the deployment of privacy protection mechanisms in social networks.