A high-concurrency blockchain model for large-scale medical cohort data storage and sharing
Xiaolin Song, Yuehan Su, Lanju Kong, Lizhen Cui, Wei Guo, Qingzhong Li · 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2022
In the medical scenarios, the demand for secure sharing of medical data and trusted federated computing continues to increase, and blockchain technology can provide secure, credible, and tamper-resistant capabilities, which makes it necessary to combine the two. However, the full application of blockchain to medical scenarios faces two issues. Medical assets such as medical cohort data with strong data correlation and large scale are difficult to be accurately described and safely operated by blockchain. Transactions such as disease diagnosis and hospitalization prediction with many parameters are difficult to execute concurrently in blockchain. Therefore, we propose MAA model, which closely associates patients with assets, separates the logic of asset operations from its storage. At the same time, we propose OPE model with double-layer pipeline concurrency. By constructing the Dependency Graph, and generating multiple blocks with low conflict rates, which are executed simultaneously among multiple Execute Groups, OPE supports the high concurrent execution of medical transactions with high computing power requirements such as trusted federated learning. Experiments show that our model supports multiple types of medical data on-chain compared to existing models, and the concurrency is increased by at least 40% in a high-conflict medical environment.