Supply chain information security sharing technology based on blockchain consensus algorithm and federated learning

Existing supply chain information security methods all suffer from the difficulty of balancing information sharing efficiency and information privacy protection. Data is prone to leakage, resulting in an increase in the overall risk of the supply chain. In response to this situation, the study proposes a supply chain information security sharing method based on a blockchain consensus algorithm and federated learning. The study designs a blockchain formula algorithm based on a verifiable mechanism and combines this algorithm with federated learning to construct an encryption model. To address the issue of privacy leakage that is prone to occur in federated learning, this study introduces casual pseudo-random functions and cuckoo hashing to process data and reduce communication complexity, thereby avoiding hash conflicts. Finally, the encryption model is applied to the data transmission system, and combined with multi-factor authentication, the secure sharing of supply chain information is achieved. The experiment results indicated that the average latency of the consensus algorithm during node election was 115.20ms, and during node replacement, the average latency was 8.56ms. The information security sharing method proposed in the study achieved an accuracy rate of 92.48% in generating data after processing, with a data tampering detection rate of 98.87%. The frequency of privacy breaches during the experiment was only 0.1%, and the average response time was 1.0 s. The study proposes a new technical framework that takes into account both privacy protection and efficient sharing, effectively balancing the trust establishment and data security requirements in multi-party collaboration in the supply chain, and providing a verifiable and traceable technical path for information sharing in complex network environments. At the same time, by optimizing the integration mode of the consensus mechanism and federated learning, the system communication overhead and response delay have been significantly reduced, and the overall operational efficiency has been improved.