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：摘要：深度学习的蓬勃发展引发了隐私与安全方面的担忧。为解决这些问题，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。然而在实践中，2PC方法通常会产生高昂的计算与通信开销，这限制了其在大规模系统中的应用。为应对这一挑战，我们提出了RRNet——一个旨在协同降低多方安全计算（MPC）比较协议开销并通过硬件加速提升计算效率的系统性框架。该方法将密码学构建模块的硬件延迟集成至深度神经网络（DNN）损失函数中，从而在能效、准确率与安全性保障方面实现改进。此外，我们还为现场可编程门阵列（FPGA）设计了一个密码学硬件调度器及其对应的性能模型，以进一步增强框架效率。实验表明，在CIFAR-10数据集上，RRNet的ReLU精简性能显著超越所有现有最优方法。