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Issue No.03 - May/June (2011 vol.15)
pp: 13-19
Danesh Irani , Georgia Institute of Technology
Steve Webb , Georgia Institute of Technology
Calton Pu , Georgia Institute of Technology
Kang Li , University of Georgia
<p>Most people have multiple accounts on different social networks. Because these networks offer various levels of privacy protection, the weakest privacy policies in the social network ecosystem determine how much personal information is disclosed online. A new information leakage measure quantifies the information available about a given user. Using this measure makes it possible to evaluate the vulnerability of a user's social footprint to two known attacks: physical identification and password recovery. Experiments show the measure's usefulness in quantifying information leakage from publicly crawled information and also suggest ways of better protecting privacy and reducing information leakage in the social Web.</p>
Social networks, personal information leakage, security and privacy
Danesh Irani, Steve Webb, Calton Pu, Kang Li, "Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks", IEEE Internet Computing, vol.15, no. 3, pp. 13-19, May/June 2011, doi:10.1109/MIC.2011.25
1. A. Lenhart, Adults and Social Network Websites, Pew Research Center, 2009; .
2. C. Johnson III, Protection of Sensitive Agency Information, US Office of Management and Budget, 2006; assets/omb/memoranda/fy2006m06-16.pdf .
3. L. Sweeney, Uniqueness of Simple Demographics in the US Population, tech. report LIDAP-WP4, Lab. for Int'l Data Privacy, Carnegie Mellon Univ., 2000.
4. K. Zetter, "Palin E-mail Hacker Says It Was Easy," Wired,18 Sept. 2008; .
5. M. Just, "Designing and Evaluating Challenge-Question Systems," IEEE Security & Privacy, vol. 2, no. 5, 2004, pp. 32–39.
6. A. Rabkin, "Personal Knowledge Questions for Fallback Authentication: Security Questions in the Era of Facebook," Proc. 4th Symp. Usable Privacy and Security, ACM Press, 2008, pp. 13–23.
7. S. Schechter, A. Brush, and S. Egelman, "It's No Secret: Measuring the Security and Reliability of Authentication via Secret Questions," Proc. IEEE Symp. Security and Privacy, IEEE CS Press, 2009; doi:10.1109/SP.2009.11.
8. A. Narayanan and V. Shmatikov, "Robust De-anonymization of Large Sparse Datasets," Proc. IEEE Symp. Security and Privacy, IEEE CS Press, 2008; doi:10.1109/SP.2008.33.
9. A. Narayanan and V. Shmatikov, "De-anonymizing Social Networks," Proc. IEEE Symp. Security and Privacy, IEEE CS Press, 2009; doi:10.1109/SP.2009.22.
10. V. Verykios, G. Moustakides, and M. Elfeky, "A Bayesian Decision Model for Cost Optimal Record Matching," Int'l J. Very Large Data Bases, vol. 12, no. 1, 2003, pp. 28–40.
11. M. Balduzzi et al., "Abusing Social Networks for Automated User Profiling," Proc. 13th Int'l Symp. Recent Advances in Intrusion Detection (RAID), Springer, 2010, pp. 422–441.
12. D. Irani et al., "Large Online Social Footprints — An Emerging Threat," Proc. Int'l Conf. Computational Science and Eng., vol. 3, IEEE Press, 2009, pp. 271–276.
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