Welcome to the resource topic for 2022/1085
Title:
Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning
Authors: Lijing Zhou, Ziyu Wang, Hongrui Cui, Qingrui Song, Yu Yu
Abstract:The overhead of non-linear functions dominates the performance of the secure multiparty computation (MPC) based privacy-preserving machine learning (PPML).
This work introduces a family of novel secure three-party computation (3PC) protocols, Bicoptor, which improve the efficiency of evaluating non-linear functions.
The basis of Bicopter is a new sign determination protocol, which relies on a clever use of the truncation protocol proposed in SecureML (S&P 2017). Our 3PC sign determination protocol only requires two communication rounds, and does not involve any preprocessing.
Such sign determination protocol is well-suited for computing non-linear functions in PPML, e.g. the activation function ReLU, Maxpool, and their variants. We develop suitable protocols for these non-linear functions, which form a family of GPU-friendly protocols, Bicopter.
All Bicoptor protocols only require two communication rounds without preprocessing.
We evaluate Bicoptor under a 3-party LAN network over a public cloud, and achieve 90,000 DReLU/ReLU or 3,200 Maxpool (find the maximum value of nine inputs) operations per second.
Under the same settings and environment, our ReLU protocol has a one or even two order(s) of magnitude improvement to the state-of-the-art works, Edabits (CRYPTO 2020) or Falcon (PETS 2021), respectively without batch processing.
ePrint: https://eprint.iacr.org/2022/1085
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