Bicoptor 2.0: Addressing Challenges in Probabilistic Truncation for Enhanced Privacy-Preserving Machine Learning

by   Lijing Zhou, et al.

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.


Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning

The overhead of non-linear functions dominates the performance of the se...

Safepaths: Vaccine Diary Protocol and Decentralized Vaccine Coordination System using a Privacy Preserving User Centric Experience

In this early draft, we present an end-to-end decentralized protocol for...

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

We introduce CryptGPU, a system for privacy-preserving machine learning ...

Efficient Privacy-Preserving Approximation of the Kidney Exchange Problem

The kidney exchange problem (KEP) seeks to find possible exchanges among...

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Machine learning has started to be deployed in fields such as healthcare...

Extracting Protocol Format as State Machine via Controlled Static Loop Analysis

Reverse engineering of protocol message formats is critical for many sec...

Lifting Network Protocol Implementation to Precise Format Specification with Security Applications

Inferring protocol formats is critical for many security applications. H...

Please sign up or login with your details

Forgot password? Click here to reset