To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles (2009)

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by Elena Zheleva , Lise Getoor

Venue:	In WWW
Citations:	21 - 0 self

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Datum	Value	Source
TITLE	To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles	INFERENCE
AUTHOR NAME	Elena Zheleva	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; University of Maryland, College Park	SVM HeaderParse 0.2
AUTHOR NAME	Lise Getoor	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Computer Science; University of Maryland, College Park; Categories and Subject Descriptors	SVM HeaderParse 0.2
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.	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE	In WWW	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	24 found	ParsCit 1.0