Empowering users through privacy management recommender systems

Preserving individuals´ integrity in the digital world is expected to be one of the major challenges of our society. This is due to the fact that the online service providers are increasingly collecting more and more personal information. In this study, we propose a privacy recommender system to help users make more appropriate decisions with regards to their privacy. Our recommender tool uses an ontology engine for parsing and comprehension of privacy policy statements, privacy settings, and user needs that are provided either directly by the user or from users´ past behaviors. The output of the system is a set of recommendations and warnings generated based on the users´ privacy preferences. We present some results of the usage of our recommender system in social networking applications such as Facebook. This privacy recommender system will provide users with a greater control of their personal data.