The goal of my research is to find out the effects of showing different people various privacy suggestions in a Facebook-like system. In theory, this understanding could provide tools that allow users who want different amounts of privacy to achieve it conveniently. To adapt to users, however, one needs a model of the users. We picked Privacy Profiles, a previously established user model. Privacy Profiles basically categorize users of a social network based on the privacy features they use and are aware of. For example, people who often block people may be categorized as Privacy Maximizers. Based on this model, we designed three ways of creating adaptations, and three ways of showing them. When generating suggestions, we can use optimization, which helps the user with things they already do, solidification, which helps them with features inside of their profile (ones which they should be using), or self-actualization, which suggests things they might not do themselves. These are called Adaptation Methods. These adaptations can then be shown in various ways, called Introduction Methods: automation implements changes without asking the user first (with an undo button), highlighting makes features more visible or prominent, and suggestion shows a Privacy Dinosaur (Based on of a similar dinosaur on Facebook) to give personalized suggestions.

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• Sociology
• Computer Science