OpenNym: Privacy preserving recommending via pseudonymous group authentication

A user accessing an online recommender system typically has two choices: either agree to be uniquely identified and in return receive a personalized and rich experience, or try to use the service anonymously but receive a degraded non-personalized service. In this paper, we offer a third option to this "all or nothing" paradigm, namely use a web service with a public group identity, that we refer to as an OpenNym identity, which provides users with a degree of anonymity while still allowing useful personalization of the web service. Our approach can be implemented as a browser shim that is backward compatible with existing services and as an example, we demonstrate operation with the Movielens online service. We exploit the fact that users can often be clustered into groups having similar preferences and in this way, increased privacy need not come at the cost of degraded service. Indeed use of the OpenNym approach with Movielens improves personalization performance.