An intelligent blockchain-based access control framework with federated learning for genome-wide association studies

【Author】 Wang, Huanhuan; Zhang, Xiao; Xia, Youbing; Wu, Xiang

【Source】COMPUTER STANDARDS & INTERFACES

【影响因子】3.721

【Abstract】Genome-Wide Association Studies (GWAS) aim to find various variations in human disorders and have become one of the most commonly-used methods to find the pathogenesis and genetic mechanisms of complex diseases. However, the GWAS process needs to frequently search the genome-wide data, especially in the calculation process of multi-party participation. The statistical value calculation and interactive search of Single Nucleotide Polymorphisms (SNPs) and model training processes might easily disclose personal information. Therefore, to solve these problems, we propose a Blockchain-based access control Framework for GWAS with Federated Learning-BFGF. Specifically, before training local models, this framework implements Automated Quality Con-trol (AQC) to guarantee the quality of training data. Design the authentication mechanism in blockchain to filter out users who are malicious attackers to protect the security of other users' information initially. To accelerate the speed of cloud model training and resist multiple attacks in federated learning, propose a periodic aggre-gation method combining differential privacy mechanisms. Finally, simulated experiments have shown that the BFGF framework can protect the security of genetic data and balance availability and accuracy.

【Keywords】Automated quality control; Federated learning; Differential privacy; Blockchain

【发表时间】2023 MAR

【收录时间】2023-01-04

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习