Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • College of Computer, Mathematical & Natural Sciences
    • Computer Science
    • Technical Reports from UMIACS
    • View Item
    •   DRUM
    • College of Computer, Mathematical & Natural Sciences
    • Computer Science
    • Technical Reports from UMIACS
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    How friendship links and group memberships affect the privacy of individuals in social networks

    Thumbnail
    View/Open
    ez-cs-tr-4922.pdf (341.9Kb)
    No. of downloads: 1276

    Date
    2008-07-01
    Author
    Zheleva, Elena
    Getoor, Lise
    Metadata
    Show full item record
    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 a 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 a simple yet powerful model that uses group features and group memberships of users to perform multi-value classification. We compare its efficacy against several other classification approaches. Our results show that even in the case when there is an option for making profile attributes private, if links and group affiliations are known, users' privacy in social networks may be compromised. On a dataset from a well-known social-media website, we could easily recover the sensitive attributes for half of the private-profile users with a high accuracy when as much as half of the profiles are private. 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. We conclude with a discussion of our findings and the broader applicability of our proposed model.
    Notes
    An updated version of this report appears as CS-TR-4926, UMIACS-TR-2008-18.
    URI
    http://hdl.handle.net/1903/8691
    Collections
    • Technical Reports from UMIACS
    • Technical Reports of the Computer Science Department

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility