Modeling privacy settings of an online social network from a game-theoretical perspective

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.

[1]  Anna Cinzia Squicciarini,et al.  WWW 2009 MADRID! Track: Security and Privacy / Session: Web Privacy Collective Privacy Management in Social Networks , 2022 .

[2]  Andrew K. C. Wong,et al.  Random Graphs: Structural-Contextual Dichotomy , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ángel Sánchez,et al.  Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics , 2009, Physics of life reviews.

[4]  K. J. Ray Liu,et al.  Secure Cooperation in Autonomous Mobile Ad-Hoc Networks Under Noise and Imperfect Monitoring: A Game-Theoretic Approach , 2008, IEEE Transactions on Information Forensics and Security.

[5]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[6]  Shouhuai Xu,et al.  Exploiting Trust-Based Social Networks for Distributed Protection of Sensitive Data , 2011, IEEE Transactions on Information Forensics and Security.

[7]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[8]  Sarah Spiekermann,et al.  Online social networks: why we disclose , 2010, J. Inf. Technol..

[9]  Fehmi Ben Abdesslem,et al.  Reliable Online Social Network Data Collection , 2012, Computational Social Networks.

[10]  Robert van Rooij,et al.  The Stag Hunt and the Evolution of Social Structure , 2007, Stud Logica.

[11]  Niki Pissinou,et al.  Game Theoretic Modeling and Evolution of Trust in Autonomous Multi-Hop Networks: Application to Network Security and Privacy , 2011, 2011 IEEE International Conference on Communications (ICC).

[12]  Marco Tomassini,et al.  Cooperation on Social Networks and Its Robustness , 2012, ArXiv.

[13]  Anna Cinzia Squicciarini,et al.  An Informed Model of Personal Information Release in Social Networking Sites , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[14]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Cliff Lampe,et al.  A familiar face(book): profile elements as signals in an online social network , 2007, CHI.

[16]  B. Bollobás.  The evolution of random graphs , 1984 .

[17]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[18]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[19]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[20]  Krishna P. Gummadi,et al.  Analyzing facebook privacy settings: user expectations vs. reality , 2011, IMC '11.

[21]  K. J. Ray Liu,et al.  Cooperation Stimulation Strategies for Peer-to-Peer Wireless Live Video-Sharing Social Networks , 2010, IEEE Transactions on Image Processing.

[22]  Cliff Lampe,et al.  The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites , 2007, J. Comput. Mediat. Commun..