结合同态加密与差分隐私，实现医疗组学数据的高效高保真联邦学习。
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
- 客户端按算力自选同态加密或差分隐私，动态平衡安全与效率。
- 在相同隐私预算下，预测准确率优于纯差分隐私方法，计算耗时显著降低。
- 适合算力差异大的医疗设备协同建模，兼顾隐私保护与模型性能。
组学数据广泛用于医学研究以揭示疾病机制，但包含高度敏感的个人健康信息。联邦学习（FL）结合差分隐私（DP）可抵御恶意用户攻击，但强隐私保护会因注入噪声而降低预测精度。同态加密（HE）支持在加密数据上直接计算，能避免DP引入的噪声，提升预测性能，但计算开销较大。为在保障隐私的同时兼顾异构客户端的计算能力，本文提出一种隐私保护机器学习（PPML-Hybrid）方法，让客户端根据自身算力自主选择使用HE或DP：高算力客户端采用无噪声的HE更新，低算力客户端则用轻量级的DP减少负担；高算力者还可依隐私需求灵活切换。在多个组学数据集上的实验表明，该方法在同等隐私预算下表现优于纯DP方案，且相比纯HE方法显著减少计算时间，同时保持高预测精度。
原文摘要 · Abstract (English)
Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of omics data privacy against malicious user attacks. However, FL with the DP method faces an inherent trade-off: stronger privacy protection degrades predictive accuracy due to injected noise. On the other hand, Homomorphic Encryption (HE) allows computations on encrypted data and enables aggregation of encrypted gradients without DP-induced noise can increase the predictive accuracy. However, it may increase the computation cost. To improve the predictive accuracy while considering the computational ability of heterogeneous clients, we propose a Privacy-Preserving Machine Learning (PPML)-Hybrid method by introducing HE. In the proposed PPML-Hybrid method, clients distributed select either HE or DP based on their computational resources, so that HE clients contribute noise-free updates while DP clients reduce computational overhead. Meanwhile, clients with high computational resources clients can flexibly adopt HE or DP according to their privacy needs. Performance evaluation on omics datasets show that our proposed method achieves comparable predictive accuracy while significantly reducing computation time relative to HE-only. Additionally, it outperforms DP-only methods under equivalent or stricter privacy budgets.
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