An Architecture for Agent-Based Privacy-Preserving Information Filtering
Recommender Systems based on Information Filtering techniques are utilized to an increasing degree in order to provide personalized information, countering information overload. Due to the antagonism of personalization and privacy, however, current Recommender System architectures are not suitable for use with extensive and sensitive user profile data. We propose a novel approach to agentbased Information Filtering resulting in an architecture preserving the privacy of all participants. The proposed solution covers trust relationships between participants and utilizes privacy-preserving implementations of existing filtering techniques.