Secure Medical Image Analysis with CrypTFlow
This work provides a tool for privacy-preserving medical image analysis, which is crucial for institutions handling sensitive patient data.
This paper introduces CRYPTFLOW, a system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols. It was used to perform secure inference on DENSENET121 for lung disease detection from chest X-rays and 3D-UNet for segmentation in radiotherapy planning, achieving the largest secure inference task to date.
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.