  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.
