Social network privacy measurement and simulation

Privacy has become an important concern in online social networks. One of the fundamental challenging issues is privacy measurement. Without a practical and effective way to quantify, measure and evaluate privacy, it is hard for social networking sites and users to make and adjust privacy settings to protect privacy. In this paper, we introduce a practical and effective approach for privacy measurement in social networks. We use Privacy Index (PIDX) to measure a user's privacy exposure in a social network. PIDX is a numerical value between 0 and 100. High PIDX value indicates high privacy risk in social networks. A privacy index function PIDX(i, j) is proposed to evaluate actor Aj's privacy exposure to actor Aj. Using this model, it is convenient to evaluate any users' privacy exposure to their friends, friends of friends, and public. We further develop a social network privacy simulation tool, OSNPIDX, to verify the effectiveness of our approach.

[1]  Yong Wang,et al.  Privacy Measurement for Social Network Actor Model , 2013, 2013 International Conference on Social Computing.

[2]  Barbara Carminati,et al.  Privacy in Social Networks: How Risky is Your Social Graph? , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[3]  E. Michael Maximilien,et al.  Privacy-asa-Service : Models , Algorithms , and Results on the Facebook Platform , 2009 .

[4]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[5]  Mark S. Ackerman,et al.  Privacy in e-commerce: examining user scenarios and privacy preferences , 1999, EC '99.

[6]  Joseph Bonneau,et al.  The Privacy Jungle: On the Market for Data Protection in Social Networks , 2009, WEIS.

[7]  Justine Becker Measuring privacy risk in online social networks , 2009 .

[8]  Ahmed K. Elmagarmid,et al.  Privometer: Privacy protection in social networks , 2010, 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010).

[9]  Yong Wang,et al.  SONET: A SOcial NETwork Model for Privacy Monitoring and Ranking , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[10]  Evimaria Terzi,et al.  A Framework for Computing the Privacy Scores of Users in Online Social Networks , 2009, 2009 Ninth IEEE International Conference on Data Mining.