Understanding privacy leakage via the social network

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 this research project, we ask the question: given attributes for some fraction of the users in an online social network, can we infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To answer this question, we gather fine-grained data from two social networks and try to infer user profile attributes. We find that users with common attributes are more likely to be friends and often form dense communities, and we propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users



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.




Saptarshi Ghosh is awarded a Humboldt Postdoctoral Research Fellowship
July 2014

Mainack Mondal, Bimal Viswanath and Krishna Gummadi, along with their co-authors win SOUPS distinguished paper award
July 2014

Juhi Kulshrestha receives Google Anita Borg Scholarship
May 2013

Cristian Danescu-Niculescu-Mizil wins WWW best paper award
May 2013