This paper primarily focuses on analyzing the problems and proposing solutions for the probabilistic truncation protocol in existing PPML works from the perspectives of accuracy and efficiency. In terms of accuracy, we reveal that precision selections recommended in some of the existing works are incorrect. We conduct a thorough analysis of their open-source code and find that their errors were mainly due to simplified implementation, more specifically, fixed numbers are used instead of random numbers in probabilistic truncation protocols. Based on this, we provide a detailed theoretical analysis to validate our views. We propose a solution and a precision selection guideline for future works. Regarding efficiency, we identify limitations in the state-of-the-art comparison protocol, Bicoptor's (S\&P 2023) DReLU protocol, which relies on the probabilistic truncation protocol and is heavily constrained by the security parameter to avoid errors, significantly impacting the protocol's performance. To address these challenges, we introduce the first non-interactive deterministic truncation protocol, replacing the original probabilistic truncation protocol. Additionally, we design a non-interactive modulo switch protocol to enhance the protocol's security. Finally, we provide a guideline to reduce computational and communication overhead by using only a portion of the bits of the input, i.e., the key bits, for DReLU operations based on different model parameters. With the help of key bits, the performance of our DReLU protocol is further improved. We evaluate the performance of our protocols on three GPU servers, and achieve a 10x improvement in DReLU protocol, and a 6x improvement in the ReLU protocol over the state-of-the-art work Piranha-Falcon (USENIX Sec 22). Overall, the performance of our end-to-end (E2E) privacy-preserving machine learning (PPML) inference is improved by 3-4 times.

翻译：本文主要聚焦于现有隐私保护机器学习（PPML）工作中概率截断协议的准确性与效率问题分析，并提出相应解决方案。在准确性方面，我们揭示了部分现有工作中推荐的精度选择存在错误。通过对其开源代码的深入分析，发现其错误主要源于简化实现——即在概率截断协议中使用固定数值替代随机数。基于此，我们提供了详细的理论分析验证观点，并提出解决方案与面向未来工作的精度选择指南。在效率方面，我们识别出目前最先进的比较协议Bicoptor（S&P 2023）中DReLU协议存在局限：该协议依赖概率截断协议，且受安全参数严格制约以避免错误，显著影响性能。为应对这些挑战，我们提出了首个非交互式确定性截断协议，替代原有概率截断协议。同时设计了非交互式模切换协议以增强协议安全性。最终，我们提供指南通过使用输入的部分比特（即关键比特）进行DReLU运算，基于不同模型参数降低计算与通信开销。借助关键比特，我们的DReLU协议性能得到进一步提升。在三台GPU服务器上的评估表明：相较于当前最优工作Piranha-Falcon（USENIX Sec 22），我们的DReLU协议实现10倍性能提升，ReLU协议实现6倍提升。整体而言，端到端（E2E）隐私保护机器学习（PPML）推理性能提升3-4倍。