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 management approaches. Second, we introduce a policy management 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 Face book application and conducted an extensive user study. Leveraging proven clustering techniques, we demonstrated a 23% reduction 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 perceptions of both improvements, which are very encouraging.