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.
@article{arxiv.2007.06855,
title = {BUNET: Blind Medical Image Segmentation Based on Secure UNET},
author = {Song Bian and Xiaowei Xu and Weiwen Jiang and Yiyu Shi and Takashi Sato},
journal= {arXiv preprint arXiv:2007.06855},
year = {2020}
}
Comments
11 pages, 2 figures, in Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020)