MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption
It addresses privacy concerns for hospitals and patients when outsourcing medical image analysis, though it appears incremental by combining existing techniques in a new domain.
The paper tackles the privacy risks of sharing sensitive medical images like chest X-rays for external ML services by proposing MedBlindTuner, a framework that uses fully homomorphic encryption and data-efficient image transformers to train models on encrypted data, achieving comparable accuracy to non-encrypted training.
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.