PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
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DOI：
10.1109/dac56929.2023.10247663
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Hongwu Peng;Shangli Zhou;Yukui Luo;Nuo Xu;Shijin Duan;Ran Ran-Ran;Jiahui Zhao;Chenghong Wang;Tong Geng;Wujie Wen;Xiaolin Xu;Caiwen Ding
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作者：
Hongwu Peng;Shangli Zhou;Yukui Luo;Nuo Xu;Shijin Duan;Ran Ran-Ran;Jiahui Zhao;Chenghong Wang;Tong Geng;Wujie Wen;Xiaolin Xu;Caiwen Ding
两方计算（2PC）有望实现隐私的深度学习（DL）。但是，基于2PC的基于2PC的隐私DL实现具有非线性操作员的高比较协议开销。这项工作提出了Pasnet，这是一个新颖的系统框架，可以通过将加密构件构建块的硬件潜伏期集成到神经体系结构搜索损失功能中，从而使低潜伏期，高能量效率和准确性以及担保2PC-DL。我们为案例研究开发了一个密码硬件调度程序和现场可编程门阵列（FPGA）的相应性能模型。实验结果表明，我们的轻度加权模型PASNET-A和重心的模型Pasnet-B达到了63 ms和228 ms的私人推断，对Imagenet的私人推断的潜伏期比Sota CryptGPU系统快147倍，并且比SOTA CryptGPU系统快40倍，并实现70.54％＆78.79％的精度和更高的能量效率。验证的PASNET模型和测试代码可以在GitHub1上找到。
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. The pretrained PASNet models and test code can be found on Github1.