Modeling Privacy Settings of an Online Social Network from a Game-Theoretical Perspective

Abstract

Users of online social networks are often required to adjust their privacy settings because of frequent changes in the users' connections as well as occasional changes in the social network's privacy policy. In this paper, we specifically model the user's behavior in the disclosure of user attributes in a possible social network from a game-theoretic perspective by introducing a weighted evolutionary game. We analyze the influence of attribute importance and network topology on the user's behavior in selecting privacy settings. Results show that users are more likely to reveal their most important attributes than less important attributes regardless of the risk. Results also show that the network topology exhibits a considerable effect on the privacy in a risk-included environment but a limited effect in a risk-free environment. The provided models and the gained results can be used to understand the influence of different factors on users' privacy choices.