MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical
  Images with Transformers and Fully Homomorphic Encryption
Prajwal Panzade Daniel Takabi Zhipeng Cai
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.