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.

翻译：机器学习（ML）的进步显著革新了医学图像分析领域，促使医院依赖外部ML服务。然而，在将胸部X光片等敏感患者数据共享给第三方时，固有的隐私风险也随之产生。针对这一问题，我们提出MedBlindTuner——一个基于全同态加密（FHE）与数据高效图像Transformer（DEiT）的隐私保护框架。MedBlindTuner能够仅在FHE加密的医学图像上训练ML模型。实验评估表明，MedBlindTuner在加密图像上训练的模型准确率与未加密图像训练的模型相当，为外包ML计算提供了安全解决方案，同时保护了患者数据隐私。据我们所知，这是首个在该领域结合数据高效图像Transformer与全同态加密的研究工作。