Using large, multi-national datasets for high-performance medical imaging AI systems requires innovation in privacy-preserving machine learning so models can train on sensitive data without requiring data transfer. Here we present PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data. We test PriMIA using a real-life case study in which an expert-level deep convolutional neural network classifies paediatric chest X-rays; the resulting model’s classification performance is on par with locally, non-securely trained models. We theoretically and empirically evaluate our framework’s performance and privacy guarantees, and demonstrate that the protections provided prevent the reconstruction of usable data by a gradient-based model inversion attack. Finally, we successfully employ the trained model in an end-to-end encrypted remote inference scenario using secure multi-party computation to prevent the disclosure of the data and the model.

将大型跨国数据集用于高性能医学成像 AI 系统需要在保护隐私的机器学习方面进行创新，以便模型可以在不需要数据传输的情况下对敏感数据进行训练。在这里，我们介绍了 PriMIA（保护隐私的医学图像分析），这是一个免费的开源软件框架，用于对医学成像数据进行差分私有、安全聚合的联邦学习和加密推理。我们使用真实案例研究测试 PriMIA，其中专家级深度卷积神经网络对儿科胸部 X 射线进行分类；生成的模型的分类性能与本地、非安全训练的模型相当。我们从理论上和经验上评估我们框架的性能和隐私保证，并证明所提供的保护可以防止通过基于梯度的模型反演攻击重建可用数据。最后，我们成功地在端到端加密远程推理场景中使用了训练好的模型，使用安全的多方计算来防止数据和模型的泄露。