Quantification Model Considering Uncertainties for Node Reputation in Trusted Networl}s
可信网络中一种考虑不确定性的节点信誉量化模型

Node reputation in trusted networks is an important trust relationship with many uncertainties. Reputation and the process of its aggregation arc companied with characteristics of fuzziness and randomness. Cloud model can scientifically describe uncertainties of reputation during the process of its aggregation. Converse cloud generation algorithm is adopted to discover the laws of fuzziness and randomness of node reputation and its aggregation process during its whole life. Obtained numeric eigenvalues of reputation cloud are to direct the quantification of node reputation in local computation widows. Based on certainty degree of service satisfaction degree, together with attenuation factor, a reputation quantifaction model was proposed. Certainty degree is a random number with stable tendency. Weights in the model are also random numbers with stable tendency related to service satisfaction degrees. The proposed reputation quantifaction model well fits with uncertainty laws of trust relationship in open networks. Simulation results show that the proposed model maintains stable output and has good anti-attacking abilities, compared with other models.