BUNET: Blind Medical Image Segmentation Based on Secure UNET
This addresses privacy-preserving machine learning for medical data, offering a practical solution for secure segmentation, though it is incremental as it builds on existing UNET and cryptographic techniques.
The paper tackles the problem of medical image segmentation under strict privacy regulations by proposing BUNET, a secure protocol using homomorphic encryption and garbled circuits, achieving up to 14x inference time reduction compared to state-of-the-art methods with negligible accuracy loss.
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a service schemes while protecting data confidentiality, in this work, we propose blind UNET (BUNET), a secure protocol that implements privacy-preserving medical image segmentation based on the UNET architecture. In BUNET, we efficiently utilize cryptographic primitives such as homomorphic encryption and garbled circuits (GC) to design a complete secure protocol for the UNET neural architecture. In addition, we perform extensive architectural search in reducing the computational bottleneck of GC-based secure activation protocols with high-dimensional input data. In the experiment, we thoroughly examine the parameter space of our protocol, and show that we can achieve up to 14x inference time reduction compared to the-state-of-the-art secure inference technique on a baseline architecture with negligible accuracy degradation.