论文标题
使用隐脚流保护医疗图像分析
Secure Medical Image Analysis with CrypTFlow
论文作者
论文摘要
我们提出CryptFlow，该系统将TensorFlow推理代码转换为安全多方计算（MPC）协议，按下按钮。为此，我们构建了两个组件。我们的第一个组件是从TensorFlow到各种MPC协议的端到端编译器。第二个组件是改进的半honest三方协议，为推理提供了重要的加速。我们通过证明了现实世界中神经网络（例如Densenet121）的安全推断，用于检测来自胸部X射线X射线图像的肺部疾病和3D-UNET，用于使用CT图像进行放射疗法计划中的细分，从而证明了系统的功能。特别是，本文提供了对3D图像的安全细分的首次评估，该任务比分类需要更强大的模型，并且是最大的安全推理任务运行。
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.
