Advancements in machine learning (ML) have significantly revolutionized medical image analysis, prompting hospitals to rely on external ML services. However, the exchange of sensitive patient data, such as chest X-rays, poses inherent privacy risks when shared with third parties. Addressing this concern, we propose MedBlindTuner, a privacy-preserving framework leveraging fully homomorphic encryption (FHE) and a data-efficient image transformer (DEiT). MedBlindTuner enables the training of ML models exclusively on FHE-encrypted medical images. Our experimental evaluation demonstrates that MedBlindTuner achieves comparable accuracy to models trained on non-encrypted images, offering a secure solution for outsourcing ML computations while preserving patient data privacy. To the best of our knowledge, this is the first work that uses data-efficient image transformers and fully homomorphic encryption in this domain.

机器学习在医学图像分析方面的进步极大地改变了医疗行业，但向第三方共享敏感患者数据（例如胸部X光片）存在固有的隐私风险。本文提出了MedBlindTuner，一个基于全同态加密（FHE）和数据高效图像变换器（DEiT）的隐私保护框架。MedBlindTuner使得机器学习模型仅在FHE加密的医学图像上进行训练。我们的实验评估表明，MedBlindTuner的准确率与在非加密图像上训练的模型相当，为外包机器学习计算提供了安全的解决方案，同时保护患者数据的隐私。据我们所知，这是在此领域中首次使用数据高效图像变换器和全同态加密的工作。

MedBlindTuner：基于Transformer和全同态加密的隐私保护生物医学图像微调