论文标题
不受信任的云中的隐私机器学习变得简单
Privacy-Preserving Machine Learning in Untrusted Clouds Made Simple
论文作者
论文摘要
我们提出了一个实用的框架，以基于受信任的执行环境（TEE）的不信任云中部署隐私机器学习（PPML）应用程序。具体而言，我们通过使用加密模型参数和加密输入数据在Intel SGX飞地中运行未修改的Pytorch ML应用程序，以保护这些秘密在REST和运行时的机密性和完整性。我们使用带有透明文件加密和基于SGX的远程证明的开源石墨烯库OS，以最大程度地减少移植工作，并无缝提供文件保护和证明。我们的方法对机器学习应用程序完全透明：开发人员和最终用户无需以任何方式修改ML应用程序。
We present a practical framework to deploy privacy-preserving machine learning (PPML) applications in untrusted clouds based on a trusted execution environment (TEE). Specifically, we shield unmodified PyTorch ML applications by running them in Intel SGX enclaves with encrypted model parameters and encrypted input data to protect the confidentiality and integrity of these secrets at rest and during runtime. We use the open-source Graphene library OS with transparent file encryption and SGX-based remote attestation to minimize porting effort and seamlessly provide file protection and attestation. Our approach is completely transparent to the machine learning application: the developer and the end-user do not need to modify the ML application in any way.
