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，我们指出现有部分文献建议的精度选择存在错误。通过对相关开源代码的深入分析，发现其误差主要源于简化的实现方式——具体而言，概率截断协议使用固定数值替代了随机数。基于此，我们通过详尽的理论分析验证了观点，并提出解决方案及面向未来研究的精度选取指南。在效率方面，我们识别出当前最先进的比较协议——Bicoptor（S&P 2023）的DReLU协议存在局限性：该协议依赖概率截断，且为避免错误而受到安全参数的严重制约，显著影响协议性能。为解决这些问题，我们首次提出非交互式确定性截断协议以替代原始概率截断协议，同时设计了非交互式模切换协议增强协议安全性。最后，我们提供了一种优化指南：通过仅使用输入的部分比特（即关键比特）执行DReLU运算，根据模型参数降低计算与通信开销。借助关键比特，所提DReLU协议的性能进一步提升。我们在三台GPU服务器上评估了协议性能：相较现有最优工作Piranha-Falcon（USENIX Sec 22），DReLU协议实现10倍性能提升，ReLU协议实现6倍性能提升。整体而言，端到端（E2E）隐私保护机器学习推理性能提升3-4倍。