DOUBLEFACE: ROBUST REPUTATION RANKING BASED ON LINK ANALYSIS IN P2P NETWORKS

In this paper, we propose the DoubleFace algorithm to infer peers’ reputation rankings, which explicitly includes two phases. First is the computation of peers’ Recommendation Ability (RA) based on the following intuitive idea: Peer's RA will be reversely determined by how many peers it points to and how bad those peers have been rated, which comprise two subroutines: the badness propagation and the conversion of RA from badness. In the first subroutine, we thoroughly consider the effect of sybils’ attack on badness propagation, which has been investigated in previous work rarely; in the second subroutine, we design two methods to convert the badness into peer's RA: the disproportional way and the logistic way. The second phase is the integration of RA into adjacent matrix used to represent P2P trust graph, to reflect peer's RA in trust propagation. The simulation results show that the disproportional and logistic DoubleFace can be robust against sybils’ and front peers’ attacks to reputation ranking and can achieve significant performance improvement in comparison with Eigentrust-like algorithms, in hostile P2P environments. Moreover, we also discuss the effect of hostile and hospitable P2P environments on the performance of DoubleFace. Specifically, disproportional DoubleFace can perform slightly better than logistic DoubleFace in hostile P2P environments, but, in hospitable P2P environments, disproportional DoubleFace performs anti-intuitively poorly, and logistic DoubleFace achieves the same performance as traditional Eigentrust-like algorithms.