Inferring private information using social network data

Authors:
Jack Lindamood;Raymond Heatherly;Murat Kantarcioglu;Bhavani Thuraisingham
Affiliations:
Facebook, Palo Alto, CA, USA;The University of Texas at Dallas, Richardson, TX, USA;The University of Texas at Dallas, Richardson, TX, USA;The University of Texas at Dallas, Richardson, TX, USA
Venue:
Proceedings of the 18th international conference on World wide web
Year:
2009

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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.