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

Published: 20 April 2009 Publication History

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.

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    cover image ACM Conferences
    WWW '09: Proceedings of the 18th international conference on World wide web
    April 2009
    1280 pages
    ISBN:9781605584874
    DOI:10.1145/1526709

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 20 April 2009

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    Author Tags

    1. attribute inference
    2. groups
    3. privacy
    4. social networks

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    • (2024)Publishing number of walks and katz centrality under local differential privacyProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702694(377-393)Online publication date: 15-Jul-2024
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