The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as a means of enabling privacy-preserving DL computation. However, in practice, 2PC methods often incur high computation and communication overhead, which can impede their use in large-scale systems. To address this challenge, we introduce RRNet, a systematic framework that aims to jointly reduce the overhead of MPC comparison protocols and accelerate computation through hardware acceleration. Our approach integrates the hardware latency of cryptographic building blocks into the DNN loss function, resulting in improved energy efficiency, accuracy, and security guarantees. Furthermore, we propose a cryptographic hardware scheduler and corresponding performance model for Field Programmable Gate Arrays (FPGAs) to further enhance the efficiency of our framework. Experiments show RRNet achieved a much higher ReLU reduction performance than all SOTA works on CIFAR-10 dataset.

翻译：深度学习（DL）的普及引发了隐私与安全方面的担忧。为了解决这些问题，安全两方计算（2PC）被提出作为实现隐私保护深度学习计算的一种手段。然而，在实际应用中，2PC方法往往带来高昂的计算和通信开销，这可能阻碍其在大型系统中的使用。为应对这一挑战，我们引入了RRNet，一个旨在通过硬件加速共同降低MPC比较协议开销并加速计算的系统性框架。我们的方法将密码学构建块的硬件延迟纳入深度神经网络损失函数，从而提升能效、准确性和安全保障。此外，我们为现场可编程门阵列（FPGA）提出了一种密码学硬件调度器及相应的性能模型，以进一步增强我们框架的效率。实验表明，在CIFAR-10数据集上，RRNet的ReLU缩减性能显著优于所有现有最优方法。