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Expandable grids for visualizing and authoring computer security policies
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BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 1
Privacy wizards for social networking sites
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PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
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SocInfo'12 Proceedings of the 4th international conference on Social Informatics
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HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: users and contexts of use - Volume Part III
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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.