Items where author is affiliated with University of Maryland
Number of items: 1.
and Getoor, Lise 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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