Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today’s online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes in their profile in a specific network. In our research we ask the question: How much information about an unknown user can we infer in a social networking site? Given information about other users in the same site is available, or even when information about a set of users in another social networking site is available? Further we also quantify the security risk in the scenario when someone actually searches to find sensitive attribute of a given user.

 

R-Susceptibility: An IR-Centric Approach to Assessing Privacy Risks for Users in Online Communities,
Joanna Asia Biega, Krishna P. Gummadi, Ida Mele, Dragan Milchevski, Christos Tryfonopoulos, and Gerhard Weikum. Annual SIGIR Conference (SIGIR), Pisa, Italy, July 2016.

On the Reliability of Profile Matching Across Large Online Social Networks,
Oana Goga, Patrick Loiseau, Robin Summer, Renata Teixeira, and Krishna P. Gummadi. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Sydney, Australia, August 2015.

You are who you know: Inferring user profiles in Online Social Networks
Alan Mislove, Bimal Viswanath, Krishna P. Gummadi, and Peter Druschel. ACM International Conference on Web Search and Data Mining (WSDM), New York, NY, February 2010.

 

Oana Goga
Alan Mislove
Bimal Viswanath
Peter Druschel
Krishna Gummadi