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Ensuring privacy of users of social networks is probably an unsolvable conundrum. It seems, however, that informed use of the existing privacy options by the social network participants may alleviate -or even prevent -some of the more drastic privacyaverse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social network behavior in terms of their privacy settings and broad privacy categories, and 2) recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents early research that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users.

Privacy is a leading concern for anyone that utilizes computing resources whether shopping on the Internet or visiting their doctor. Legislative acts require enterprises and data collectors to protect the privacy of their customers and data owners. Although privacy policy frameworks such as P3P assist data collectors in demonstrating their privacy policies to customers (i.e. publishing privacy policy on websites), insufficient research has been reported to help users visualize privacy policies. This paper presents a privacy policy visualization model based on the predicates of a privacy policy model. The key contribution is to provide a visualization model that facilitates understanding the policies for the data owners and provides the opportunity for the policy officers to better understand the designed policies. Finally, we demonstrate the model with a use case drawn from the policies of an online social network.

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