Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNet, a novel systematic framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by integrating the hardware latency of the cryptographic building block into the neural architecture search loss function. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) as a case study. The experimental results demonstrate that our light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms and 228 ms latency on private inference on ImageNet, which are 147 and 40 times faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and more than 1000 times higher energy efficiency.

翻译：两方计算（2PC）有望实现隐私保护的深度学习（DL）。然而，基于2PC的隐私保护深度学习实现因非线性算子而面临较高的比较协议开销。本文提出PASNet——一种新颖的系统性框架，通过将密码学构建模块的硬件延迟纳入神经架构搜索损失函数，实现低延迟、高能效与高精度、且具备安全性保障的2PC-DL。我们以现场可编程门阵列（FPGA）为例，开发了密码学硬件调度器及相应的性能模型。实验结果表明，我们的轻量模型PASNet-A与重量模型PASNet-B在ImageNet私有推理上分别达到63毫秒与228毫秒延迟，较现有最佳系统CryptGPU加速147倍与40倍，同时实现70.54%与78.79%的准确率，并提升超过1000倍的能效。