Machine Learning as a Service (MLaaS) is an increasingly popular design where
a company with abundant computing resources trains a deep neural network and
offers query access for tasks like image classification. The challenge with
this design is that MLaaS requires the client to reveal their potentially
sensitive queries to the company hosting the model. Multi-party computation
(MPC) protects the client's data by allowing encrypted inferences. However,
current approaches suffer prohibitively large inference times. The inference
time bottleneck in MPC is the evaluation of non-linear layers such as ReLU
activation functions. Motivated by the success of previous work co-designing
machine learning and MPC aspects, we develop an activation function co-design.
We replace all ReLUs with a polynomial approximation and evaluate them with
single-round MPC protocols, which give state-of-the-art inference times in
wide-area networks. Furthermore, to address the accuracy issues previously
encountered with polynomial activations, we propose a novel training algorithm
that gives accuracy competitive with plaintext models. Our evaluation shows
between $4$ and $90\times$ speedups in inference time on large models with up
to $23$ million parameters while maintaining competitive inference accuracy.

介绍了一种基于多方计算的机器学习服务（MLaaS）的方法，通过使用多项式逼近替换 ReLU 激活函数和采用单轮 MPC 协议进行评估，实现了在大型模型上快速准确的推理，从而保护客户数据隐私。