Efficient Distributed Privacy-Preserving Reputation Mechanism Handling Non-Monotonic Ratings

—Open and large-scale systems do not encourage their users to behave trustworthily, which may entail non-negligible risks when interacting with unknown users, for instance when buying an item on an e-commerce platform. Reputation mech-anisms reduce these risks by associating a reputation score to each user, summarizing their past behavior. To be useful to users, reputation mechanisms need to guarantee two main properties: the non-monotonicity of reputation scores, in order to exactly reflect the users' behavior, and the privacy of their users, so that the history of their transactions is not publicly available. We propose a distributed privacy-preserving reputation mechanism handling non-monotonic ratings. Our proposition relies on two distinct distributed third parties and on cryptographic tools, including zero-knowledge proofs of knowledge, anonymous proxy signatures, and verifiable secret sharing. We show that this proposal is computationally efficient, and thus practical. To the best of our knowledge, this solution is the first one that preserves users' privacy and handles both positive and negative ratings without relying on a central authority