Hyperledger Fabric Consensus Optimization Method Based on Endorsing Nodes

Aiming at a series of security risks caused by the transparency and scarcity of endorsing nodes information in Hyperledger fabric, A non-interactive and verifiable optimization scheme for randomly selecting endorsing nodes is proposed, That is, the fabric model based on endorsement-ordering-verification. By introducing the candidate set of endorsing nodes and using the verifiable random function based on the elliptic curve SM2 algorithm to randomly select endorsing nodes as transactions for endorsement strategy, The model realizes the non-interactive random selection function of endorsing nodes in fabric, which can effectively prevent hacker attacks on the network. Experimental results show that the proposed scheme has higher security than the comparison method and the scheme before optimization.