A Distributed, Penetrative and All-Dimensional Supervision Architecture for Consortium Blockchain

Consortium blockchain has been used widely in the field of bank, insurance, securities, commerce association and enterprise. However, the supervision for consortium blockchain faces serious challenges: Firstly, due to the sealied feature of consortium blockchain, it should build up the effective and robust computation mechanism for distributed supervision between various platforms; Secondly, due to the sensitivity of supervision data, data transmission between various platforms introduces the asset privacy disclosure and equity losses. In addition, due to the heterogeneity of supervision data, it should build up an effective data access and management mechanism. To overcome these challenges, we propose a novel supervision architecture for consortium blockchain. Specifically, first, we build up a federal-learning-based distributed supervision computation system, which leverages the committee mechanism and sharding to enable the highly robust and efficient computation mechanism for supervision data. Second, we build up a zero-knowledge-proof- based penetrative cross-chain evidencing method, which leverages the attribute encryption, Merkle tree and authorization hiding method to enable the highly reliable data transmission mechanism for consortium blockchain. Third, we build up an all-dimensional data access and management method, to enable the highly effective supervision data extraction for consortium blockchain. In summary, we propose a consortium-blockchain-oriented distributed penetrating and all-dimensional architecture, which satisfies the cross-organization supervision demand in consortium blockchain supervision.