Privacy Measurement for Social Network Actor Model

Privacy measurement is critical to evaluate privacy risks for business, public policy and legislation. However, there is a lack of effective and practical way to quantify, measure, and evaluate privacy. In this paper, we propose three privacy indexes for privacy measurement (ranking) for social network actor model, i.e., weighted privacy index (w-PIDX), maximum privacy index (m-PIDX), and composite privacy index (c-PIDX). We also introduce a novel virtual attribute for social network actor model to describe the combined attributes' behavior. We further evaluate and demonstrate the effectiveness of these PIDXes for various user groups in different testing scenarios. Our tests and analysis show that composite privacy index, c-PIDX, is the best to measure privacy for social network actor model. A practical approach to evaluate w-PIDX, m-PIDX, and c-PIDX is also presented in the paper.

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

[2]  Philippe Golle,et al.  Revisiting the uniqueness of simple demographics in the US population , 2006, WPES '06.

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

[4]  Bobby Bhattacharjee,et al.  Persona: an online social network with user-defined privacy , 2009, SIGCOMM '09.

[5]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

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

[7]  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.

[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]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Jian Pei,et al.  Preserving Privacy in Social Networks Against Neighborhood Attacks , 2008, 2008 IEEE 24th International Conference on Data Engineering.

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

[12]  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.

[13]  Vitaly Shmatikov,et al.  De-anonymizing Social Networks , 2009, 2009 30th IEEE Symposium on Security and Privacy.

[14]  Lise Getoor,et al.  To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles , 2009, WWW '09.

[15]  Kevin Borders,et al.  Social networks and context-aware spam , 2008, CSCW.

[16]  Francesco Buccafurri,et al.  Privacy-preserving resource evaluation in social networks , 2012, 2012 Tenth Annual International Conference on Privacy, Security and Trust.

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

[18]  Frank Stajano,et al.  Privacy-enabling social networking over untrusted networks , 2009, WOSN '09.

[19]  Christopher Krügel,et al.  A Practical Attack to De-anonymize Social Network Users , 2010, 2010 IEEE Symposium on Security and Privacy.

[20]  Jie Tang,et al.  Inferring social ties across heterogenous networks , 2012, WSDM '12.

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

[22]  A. Perrig,et al.  Exploiting Privacy Policy Conflicts in Online Social Networks (CMU-CyLab-12-005) , 2011 .