论文标题
Bicoptor：两轮安全的三方非线性计算，而无需预处理隐私机器学习
Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning
论文作者
论文摘要
非线性函数的开销主导了基于安全多方计算（MPC）基于隐私的机器学习（PPML）的性能。这项工作介绍了一个新型安全的三方计算（3PC）方案Bicoptor家族，该方案提高了评估非线性函数的效率。 Bicoptor的基础是一种新的标志确定协议，该协议依赖于Secureml中提出的截断协议的巧妙使用（S \＆P 2017）。我们的3PC标志确定协议仅需要两个通信回合，并且不涉及任何预处理。这种标志确定协议非常适合计算PPML中的非线性函数，例如激活函数relu，maxpool及其变体。我们为这些非线性函数开发了合适的协议，这些函数构成了对GPU友好型方案的家族。所有Bicoptor协议都只需要两个通信，而无需预处理。我们通过公共云通过三方LAN网络评估Bicoptor，并实现370,000多个Drelu/relu或41,000 Maxpool（找到9个输入的最大值）操作。在相同的设置和环境下，我们的Relu协议分别对最先进的作品，Falcon（Pets 2021）或Edabits（Crypto 2020）有一个甚至两个数量级的改进，而无需批处理处理。
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 Bicoptor 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, Bicoptor. 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 more than 370,000 DReLU/ReLU or 41,000 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 orders of magnitude improvement to the state-of-the-art works, Falcon (PETS 2021) or Edabits (CRYPTO 2020), respectively without batch processing.
