Node Reputation Value Optimization Method Combining Recommendation Codes and Quantum Random Numbers in Blockchain

this paper proposed a cross-chain multi-dimensional reputation model based on block-chain to deal with the nodes’ risk assessment in block-chain. When the nodes joined the network, the block-chain was difficult to evaluate these nodes’ behavioral changes especially those happened in recent time. This model comprehensively took the individual behavior and transaction history of enterprise nodes, and adjusted the reputation value of the initial node dynamically by means of the recommendation code. Meanwhile, the reputation model has been brought in a liability sharing mechanism to construct a multi-level and dynamic reputation evaluation mechanism. At same time, the quantum random number could be used in this model to distinguish the malicious nodes from the honest ones. Finally, the test of the improved multi-dimensional reputation model scheme was implemented on the Fabric simulation system. The experimental results show that this model could effectively identify the possible deception behavior of nodes after joining the block-chain, improve the overall security and the success rate of transactions, enhance the users’ trust.