【Author】
Liu, Wei; Zhang, Ying-Hui; Li, Yi-Fei; Zheng, Dong
【Source】CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
【Abstract】With the rapid development of smart health, the privacy problem of medical data has become more prominent. Aiming at the problem of mining the potential value of medical data and realizing secure sharing, a fine-grained medical data sharing scheme based on federated learning is proposed. The scheme uses collaboration-oriented attribute-based encryption technologies to formulate fine-grained access strategies, allowing medical institutions or doctors to decrypt individually or collaboratively with certain conditions to achieve the purpose of accurately screening the required medical data. In the proposed scheme, the model parameters are shared such that the screened medical data is modeled and analyzed based on federated learning, which allows more people to enjoy top medical resources. In addition, a blockchain-based incentive mechanism is used to reward medical institutions which are either honest with high-quality or helpful in decryption. Hence, the enthusiasm of various medical institutions to screen data and participate in federal learning is improved. Finally, security analysis shows that the scheme is secure, and theoretical analysis and simulation test show the practicability of the scheme.
【Keywords】ABE; blockchain; collaborative decryption; federated learning; medical data mining
【摘要】随着智慧健康的快速发展，医疗数据的隐私问题更加突出。针对挖掘医疗数据潜在价值并实现安全共享的问题，提出了一种基于联邦学习的细粒度医疗数据共享方案。该方案采用面向协作的基于属性的加密技术，制定细粒度的访问策略，允许医疗机构或医生在一定条件下单独或协同解密，以达到准确筛选所需医疗数据的目的。该方案通过共享模型参数，对筛选出来的医疗数据进行基于联邦学习的建模分析，让更多人享受到顶级医疗资源。此外，基于区块链的激励机制，用于奖励诚实优质或有助于解密的医疗机构。由此，提高了各医疗机构筛选数据、参与联邦学习的积极性。最后，安全分析表明该方案是安全的，理论分析和仿真测试表明了该方案的实用性。
【关键词】安倍;区块链；协同解密；联邦学习；医学数据挖掘
【文献类型】Article; Early Access
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