@inproceedings{alvarez-valle2020secure,
author = {Alvarez-Valle, Javier and Bhatu, Pratik and Chandran, Nishanth and Gupta, Divya and Nori, Aditya and Rastogi, Aseem and Rathee, Mayank and Sharma, Rahul and Ugare, Shubham},
title = {Secure Medical Image Analysis with CrypTFlow},
booktitle = {NeurIPS PPML Workshop},
year = {2020},
month = {December},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/secure-medical-image-analysis-with-cryptflow/},
}