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...

• Type: Conference
• Year: 2010
• Where: CCS
• Authors: Lujun Fang, Heedo Kim, Kristen LeFevre, Aaron Tami
• Keywords: CCS 2010, Privacy, Privacy Preferences, Privacy Settings, Security Privacy