WWW2009 EPrints

To Join or Not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles

This item is a Paper in the Security and Privacy track.

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Abstract

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.

Fun web stuff for this record

RKBExplorer (from linked data workshop)
URI: http://eprints.rkbexplorer.com/id/www2009/eprints-54
Browse the data for this paper at RKBExplorer
REST Interface
http://www2009.eprints.org/cgi/rest/eprint/54/
ORE Resource Map
ORE was described in the Linked Data Workshop. View Resource Map
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This website has been set up for WWW2009 by Christopher Gutteridge of the University of Southampton, using our EPrints software.

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OAI:
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Handy Tools

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Social tool links
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When demoing live websites, use this tool to shorten the current URL and make it appaer real big, your audience can then easily type in the short URL and get to the same page as you. Available as a javascript bookmark