Learning Whom to Trust in a Privacy-Friendly Way

The topics of trust and privacy are more relevant to users of online communities than ever before. Trust models provide excellent means for supporting users in their decision making process. However, those models require an exchange of information between users, which can pose a threat to the users' privacy. In this paper, we present a novel approach for a privacy preserving computation of trust. Besides preserving the privacy of the recommenders by exchanging and aggregating recommendations under encryption, the proposed approach is the first that enables the trusting entities to learn about the trustworthiness of their recommenders at the same time. This is achieved by linking the minimum amount of information that is required for the learning process to the actual recommendation and by using zero-knowledge proofs for assuring the correctness of this additional information.