9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

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

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2013.254054,
        author={Jundong Chen and Matthias R. Brust and Ankunda Kiremire and Vir Phoha},
        title={Modeling Privacy Settings of an Online Social Network from a Game-Theoretical Perspective},
        proceedings={9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={ICST},
        proceedings_a={COLLABORATECOM},
        year={2013},
        month={11},
        keywords={game theory social network privacy setting network topology},
        doi={10.4108/icst.collaboratecom.2013.254054}
    }
    
  • Jundong Chen
    Matthias R. Brust
    Ankunda Kiremire
    Vir Phoha
    Year: 2013
    Modeling Privacy Settings of an Online Social Network from a Game-Theoretical Perspective
    COLLABORATECOM
    IEEE
    DOI: 10.4108/icst.collaboratecom.2013.254054
Jundong Chen,*, Matthias R. Brust1, Ankunda Kiremire1, Vir Phoha1
  • 1: Louisiana Tech University
*Contact email: jdc074@latech.edu

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.