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and Heatherly, Raymond
and Kantarcioglu, Murat
and Thuraisingham, Bhavani Inferring Private Information Using Social Network Data.
On-line social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed private information. In this paper, we explore how to launch inference attacks using released social networking data to predict undisclosed private information about individuals. We then explore the effectiveness of possible sanitization techniques that can be used to combat such inference attacks under different scenarios. social network data could be used to predict some individual private trait that a user is not willing to disclose (e.g., political or religious affiliation) and explore the effect of possible data sanitization alternatives on preventing such private information leakage. To our knowledge this is the ﬁrst comprehensive paper that discusses the problem of inferring private traits using real-life social network data and possible sanitization approaches to prevent such inference. First, we present a ıve modiﬁcation of Na¨ Bayes classiﬁcation that is suitable for classifying large amount of social network data. Our modiﬁed Na¨ Bayes algorithm predicts privacy sensitive trait ıve information using both node traits and link structure. We compare the accuracy of our learning method based on link structure against the accuracy of our learning method based on node traits. Please see extended version of this paper  for further details of our modiﬁed Naive Bayes classiﬁer. In order to protect privacy, we sanitize both trait (e.g., deleting some information from a user’s on-line proﬁle) and link details (e.g., deleting links between friends) and explore the effect they have on combating possible inference attacks. Our initial results indicate that just sanitizing trait information or link information may not be enough to prevent inference attacks and comprehensive sanitization techniques that involve both aspects are needed in practice. Similar to our paper, in , authors consider ways to infer private information via friendship links by creating a Bayesian Network from the links inside a social network. A similar privacy problem for online social networks is discussed in . Compared to  and , we provide techniques that help in choosing the most effective traits or links that need to be removed for protecting privacy.
About this site
This website has been set up for WWW2009 by Christopher Gutteridge of the University of Southampton, using our EPrints software.
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It's impractical to add all the workshops at WWW2009 by hand, but if you can provide me with the metadata in a machine readable way, I'll have a go at importing it. If you are good at slinging XML, my ideal import format is visible at http://www2009.eprints.org/import_example.xml
We (Southampton EPrints Project) intend to preserve the files and HTML pages of this site for many years, however we will turn it into flat files for long term preservation. This means that at some point in the months after the conference the search, metadata-export, JSON interface, OAI etc. will be disabled as we "fossilize" the site. Please plan accordingly. Feel free to ask nicely for us to keep the dynamic site online longer if there's a rally good (or cool) use for it...
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These are not directly related to the EPrints set up, but may be of use to delegates.
- Social tool links
- I've put links in the page header to the WWW2009 stuff on flickr, facebook and to a page which will let you watch the #www2009 tag on Twitter. Not really the right place, but not yet made it onto the main conference homepage. Send me any suggestions for new links.