document: To join or not to join : the illusion of privacy in social networks with mixed public and private user profiles In order to address privacy concerns , many social media websites allow users to hide their personal profiles from the public . In this work , we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users . We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information ( which is often not hidden ) to infer sensitive attributes . The key novel idea is that in addition to friendship links , groups can be carriers of significant information . We show that on several well-known social media sites , we can easily and accurately recover the information of private-profile users . To the best of our knowledge , this is the first work that uses link-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles .
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