Jack Lindamood
ABSTRACT
On-line social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed private information. In this paper, we explore how to launch inference attacks using released social networking data to predict undisclosed private information about individuals. We then explore the effectiveness of possible sanitization techniques that can be used to combat such inference attacks under different scenarios.
- Facebook Beacon, 2007. http://blog.facebook.com/blog.php?post=7584397130.Google Scholar
- J. He, W. Chu, and V. Liu. Inferring privacy information from social networks. In Mehrotra, editor, Proceedings of Intelligence and Security Informatics, volume LNCS 3975, 2006. Google ScholarDigital Library
- R. Heatherly, M. Kantarcioglu, J. Lindamood, and B. Thuraisingham. Preventing private information inference attacks on social networks. Technical Report UTDCS-03-09, University of Texas at Dallas, 2009.Google Scholar
- E. Zheleva and L. Getoor. To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In WWW, 2009. Google ScholarDigital Library
Index Terms
- Inferring private information using social network data
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