Cloud deep learning platforms provide cost-effective deep neural network
(DNN) training for customers who lack computation resources. However, cloud
systems are often untrustworthy and vulnerable to attackers, leading to growing
concerns about model privacy. Recently, researchers have sought to protect data
privacy in deep learning by leveraging CPU trusted execution environments
(TEEs), which minimize the use of cryptography, but existing works failed to
simultaneously utilize the computational resources of GPUs to assist in
training and prevent model leakage. This paper presents Tempo, the first
cloud-based deep learning system that cooperates with TEE and distributed GPUs
for efficient DNN training with model confidentiality preserved. To tackle the
challenge of preserving privacy while offloading linear algebraic operations
from TEE to GPUs for efficient batch computation, we introduce a customized
permutation-based obfuscation algorithm to blind both inputs and model
parameters. An optimization mechanism that reduces encryption operations is
proposed for faster weight updates during backpropagation to speed up training.
We implement Tempo and evaluate it with both training and inference for two
prevalent DNNs. Empirical results indicate that Tempo outperforms baselines and
offers sufficient privacy protection.

Tempo 是首个与 TEE 和分布式 GPU 合作的基于云的深度学习系统，通过引入以排列为基础的隐匿算法以盲化输入和模型参数，并提出了一种优化机制来减少加密操作，从而在保护模型隐私的同时加速了深度神经网络的训练，实验证明 Tempo 优于基准方法并提供足够的隐私保护。