Research On Privacy Protection In Social Networks Based On Game Theory

Social networks have become popular platforms for people to share information and communicate with others.A large amount of user-related data are generated and stored in these platforms.These data reveal user privacy details,posing a risk of privacy breach.The issue of privacy protection in social networks has gradually attracted researchers' attention.Most technologies of privacy protection focus on specific mechanisms.They ignore the loss of protection efficiency caused by lack of incentives and the impact of decisions of security behavior between users on the deployment of these mechanisms.Game theory is an important mathematical tool to study the incentives and interactions among users.We study the incentive of privacy setting and optimal investment of privacy protection in social networks based on game theory by proposing two models.They are the model of evolutionary game based analysis for user privacy protection behaviors and the model of interdependent security game based decisions for user privacy investments.Firstly,we,from the perspective of platform,propose an evolutionary game based model to analyze the impact of user behaviors on the deployment of privacy protection mechanisms.Most research on privacy protection focus on specific techniques,which assume that users adopt security protection mechanisms for granted.The impact of user behaviors on the deployment of these mechanisms is usually neglected.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.Whether to take privacy settings is used as game strategies in the model.Considering real interactions,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.Experiments in real social networks verify the feasibility of the proposed model.Based on the model of network evolutionary game,we expand the binary strategies into the continuous strategy and establish heterogeneous utility functions with different users.From the perspective of users,we propose an interdependent security game model and establish a decisionmaking framework for user privacy protection investment under the interdependent network.Most privacy settings provide many security levels to users.Users set different granularities according their demands of privacy protection.It means that different users have different investments for security.Because of connections and interactions,the users' data are related to other users.Therefore,Users can benefit from their neighbors' security investments.In the interdependent network,our investment framework firstly calculates the influence matrix,and then establish the interdependent security game model.The existence and uniqueness of Nash equilibrium(NE)are proved.Based on the game model,the theoretical solution of NE is derived.Finally,an iterative method is proposed to calculate the optimal investment strategy for each user.Experimental results in real dataset show the iterative method can converge to the theoretical solution.