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A privacy recommendation wizard for users of social networking sites

11 years 1 months ago
A privacy recommendation wizard for users of social networking sites
Privacy is a huge problem for users of social networking sites. While sites like Facebook allow individual users to personalize fine-grained privacy settings, this has proven quite difficult for average users. This demonstration illustrates a machine learning privacy wizard, or recommendation tool, that we have built at the University of Michigan. The wizard is based on the underlying observation that real users conceive their privacy preferences (which friends should see which data items) based on an implicit structure. Thus, after asking the user a limited number of carefully-chosen questions, it is usually possible to build a machine learning model that accurately predicts the user's privacy preferences. This model, in turn, can be used to recommend detailed privacy settings for the user. Our demonstration wizard runs as a third-party Facebook application. Conference attendees will be able to "test-drive" the wizard by installing it on their own Facebook accounts. Ca...
Lujun Fang, Heedo Kim, Kristen LeFevre, Aaron Tami
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where CCS
Authors Lujun Fang, Heedo Kim, Kristen LeFevre, Aaron Tami
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