document: Privacy wizards for social networking sites Privacy is an enormous problem in online social networking sites . While sites such as Facebook allow users fine-grained control over who can see their profiles , it is difficult for average users to specify this kind of detailed policy . In this paper , we propose a template for the design of a social networking privacy wizard . The intuition for the design comes from the observation that real users conceive their privacy preferences ( which friends should be able to see which information ) based on an implicit set of rules . Thus , with a limited amount of user input , it is usually possible to build a machine learning model that concisely describes a particular user 's preferences , and then use this model to configure the user 's privacy settings automatically . As an instance of this general framework , we have built a wizard based on an active learning paradigm called uncertainty sampling . The wizard iteratively asks the user to assign privacy `` labels '' to selected ( `` informative '' ) friends , and it uses this input to construct a classifier , which can in turn be used to automatically assign privileges to the rest of the user 's ( unlabeled ) friends . To evaluate our approach , we collected detailed privacy preference data from 45 real Facebook users . Our study revealed two important things . First , real users tend to conceive their privacy preferences in terms of communities , which can easily be extracted from a social network graph using existing techniques . Second , our active learning wizard , using communities as features , is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools .
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