(untitled) (International Journal of Engineering and Computer Science, 2013)
Online social networks such as Twitter, Flickr, or the Facebook have experienced exponential growth in membership in recent years. These networks offer attractive means for interaction and communication, but also raise privacy and security concerns. These online platforms allow third-party applications such as games, and productivity applications access to user online private data. Such accesses must be authorized by users at installation time. The Open Authorization protocol (OAuth) was introduced as a secure and efficient method for authorizing third-party applications without releasing a user’s access credentials but fails to provide fine-grained access control. We propose an extension to the OAuth 2.0 authorization that enables the provisioning of fine-grained authorization recommendations when granting permissions to third party applications using multi-criteria recommender system. The Recommender system utilizes application based, user-based, and category-based collaborative filtering mechanisms. Our collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. We implemented our proposed OAuth extension as a browser extension that allows users to easily configure their privacy settings at application installation time, provides recommendations on requested privacy permissions, and collects data regarding user preferences.