FineTrust: A fine-grained trust model for peer-to-peer networks

Trust research is a key issue in peer-to-peer (P2P) networks. Reputation-based trust models as one of the good solutions to resolve the trust problems in P2P network are received more and more attention in recent years. One of the fundamental challenges is to capture the evolving nature of a trust relationship between peers and reflect the varied bias or preference of peers in a distributed and open environment. In this paper, we present a fine-grained trust computation model for P2P networks. Our model defines the service as a fined-grained quality-of-service (QoS) (N-dimensional vector), and in order to accurate the recommendation trust computing, several concepts are introduced to reflect the recommenders' current status, history behavior, and the gap between these two behaviors. Also, we firstly introduce the Gauss-bar function to measure the preference similarity between peers. All these will result in a flexible model which represents trust in a manner more close to human intuitions and satisfies the diverse QoS requirements of peers in P2P networks. The extensive simulations have confirmed the efficiency of our model.