Traditional group based policy management approach for online social networks

Abstract

We introduce two approaches for improving privacy policy management in online social networks. First, we introduce a mechanism using proven clustering techniques that assists users in grouping their friends for group based policy man agement approaches. Second, we introduce a policy manage ment approach that leverages a user’s memory and opinion of their friends to set policies for other similar friends. We refer to this new approach as Same-As Policy Management. To demonstrate the effectiveness of our policy management improvements, we implemented a prototype Facebook appli cation and conducted an extensive user study. Leveraging proven clustering techniques, we demonstrated a 23% reduc tion in friend grouping time. In addition, we demonstrated considerable reductions in policy authoring time using Same As Policy Management over traditional group based policy management approaches. Finally, we presented user percep tions of both improvements, which are very encouraging