Abstract:

Federated learning enhances data sharing and collaboration between healthcare institutions, thereby improving the accuracy and efficiency of medical diagnoses, treatments, and predictions. However, existing federated learning solutions face security and efficiency challenges. Model parameter updates during training may inadvertently disclose information about local training datasets. To ensure parameter confidentiality, researchers have proposed various solutions such as masking protocols and differential privacy. However, masking protocols often lack strong security, whereas differential privacy leads to tradeoffs between accuracy and privacy. To address these challenges, this study proposes a secure federated learning scheme for smart healthcare based on secret sharing and homomorphic encryption. This scheme effectively prevents both healthcare clouds and clients from stealing model parameters and resists collusion attacks among participants. In addition, a ciphertext verification algorithm is used to ensure that model parameters can be verified during training. Security and performance analyses demonstrate that our scheme meets the confidentiality and integrity requirements for model parameters in smart healthcare scenarios, with significant improvements in computational and transmission efficiency compared to existing solutions.

Key words: smart healthcare, federated learning, homomorphic encryption, secret sharing, ciphertext verifiable

摘要：

联邦学习促进了不同医疗机构之间的数据共享和合作, 提高了医疗诊断、治疗和预测的准确性和效率。然而在医疗场景中, 现有联邦学习方案仍然存在安全和效率上的问题。在训练过程中, 模型参数的更新可能会间接地泄露有关本地训练数据集的信息。为了保证模型参数的机密性, 研究人员提出了各种解决方案, 例如掩码协议和差分隐私。使用掩码协议的解决方案通常不具有较高的安全性, 而使用差分隐私的解决方案则需要在准确性和隐私性之间进行权衡。为了解决上述挑战, 提出一种智慧医疗中基于秘密共享和同态加密的安全联邦学习方案。在模型训练过程中, 该方案能够有效抵御医疗云和医疗客户端对模型参数的窃取, 同时可以抵抗多个参与方的共谋攻击。最后, 通过密文验证算法, 确保模型参数在训练过程中的可验证性。安全性和性能分析结果表明, 该方案在智慧医疗场景中可以满足模型参数的机密性和完整性要求, 与现有方案相比, 在计算效率和传输效率上也有显著提升。

关键词: 智慧医疗, 联邦学习, 同态加密, 秘密共享, 密文可验证