  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.
