User-Centric Privacy for Identity Federations Based on a Recommendation System

Specifications such as SAML, OAuth, OpenID Connect and Mobile are essential for solving identification, authentication authorisation in contexts mobile apps, social networks, e-commerce, cloud computing or the Internet of Things. However, end-users relying on identity providers to access resources, applications services lose control over Personally Identifiable Information (PII) they share with different composing federations. This work proposes a user-centric approach based recommendation system support users making privacy decisions selecting service choosing their settings. The proposed Privacy Advisor gives protection by providing personalised recommendations without compromising federations’ functionalities requiring any changes underlying specifications. A proof concept is presented validate evaluate its utility feasibility.