University of North Alabama
Online social networking sites are experiencing tremendous user growth with hundreds of millions of active users. As a result, there is a tremendous amount of user profile data online, e.g., name, birth date, etc. Protecting this data is a challenge. The task of access policy composition is a tedious and confusing effort for the average user having hundreds of friends. In this paper, the authors propose a PolicyManager (PolicyMgr) Framework for social networks. PolicyMgr assists users in composing and managing their access control policies for objects posted to their profiles. Their approach is based on a supervised learning mechanism that leverages user provided example policy settings as training sets to build classifiers that are the basis for auto-generated policies.