We present CRYPTFLOW, a system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build two components. Our first component is an end-to-end compiler from TensorFlow to a variety of MPC protocols. The second component is an improved semi-honest 3-party protocol that provides significant speedups for inference. We empirically demonstrate the power of our system by showing the secure inference of real-world neural networks such as DENSENET121 for detection of lung diseases from chest X-ray images and 3D-UNet for segmentation in radiotherapy planning using CT images. In particular, this paper provides the first evaluation of secure segmentation of 3D images, a task that requires much more powerful models than classification and is the largest secure inference task run till date.
@article{arxiv.2012.05064,
title = {Secure Medical Image Analysis with CrypTFlow},
author = {Javier Alvarez-Valle and Pratik Bhatu and Nishanth Chandran and Divya Gupta and Aditya Nori and Aseem Rastogi and Mayank Rathee and Rahul Sharma and Shubham Ugare},
journal= {arXiv preprint arXiv:2012.05064},
year = {2020}
}
Comments
6 pages. PPML NeurIPS 2020 Workshop, Vancouver, Canada. arXiv admin note: substantial text overlap with arXiv:1909.07814