  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.
