Fine-grained privacy-preserving reputation system for Online Social Networks

Recently, Online Social Networks (OSNs) have rev-olutionarily changed the way people connect with each other and share information. The key to this success is reputation systems which enable strangers to establish trust based on the past experience. Existing reputation systems assess general reputation purely relying on the real identity rather than the behaviors of users, e.g., the content or comments posted. Furthermore, the general reputation cannot clearly depict the massive and distinct behaviors of one particular user. In this paper, we propose a fine-grained attribute-based reputation system which represents users´ reputation in terms of attributes and enables users to rate each other´s attributes instead of real identities. Our scheme verifies users´ attributes used to derive the reputation, and further uses the verified information to vote on the posted attribute-associated messages, where the verification process provides authenticity of the reputation value while not revealing the actual value to entities who do not have the privilege. To the best of our knowledge, we are the first to define a fine-grained reputation value based on users´ verified attributes in OSNs with privacy preservation. We also present the security analysis along with the simulation results to verify both the privacy-preservation and practicality of our proposed scheme.