Private distributed scalar product protocol with application to privacy-preserving computation of trust

Abstract

In this paper, we first present a private distributed scalar product protocol that can be used for obtaining trust values from private recommendations. Our protocol allows Alice to infer the trustworthiness of Bob based on what Alice's friends think about Bob and Alice's confidence in her friends. In addition, the private information of Alice and her friends are not revealed during the computation. We also propose a credential-based trust model where the trustworthiness of a user is computed based on his or her affiliations and role assignments. The trust model is simple to compute, yet it is scalable as it classifies large groups of users. © 2007 International Federation for Information Processing.