标题： EVA-S2PLoR：一种在异构数据库上实现安全逐元素乘法与逻辑回归的方法 显示英文标题

标题： EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database

摘要： 精确的非线性计算是隐私保护机器学习（PPML）中的关键挑战。 现有的大多数框架通过线性操作来近似它，导致了显著的精度损失。 本文提出了一种高效、可验证且精确的安全两方逻辑回归框架（EVA-S2PLoR），通过一种新颖的安全逐元素乘法协议及其衍生协议实现了精确的非线性函数计算。 我们的框架主要包括基于数据伪装技术的安全两方向量逐元素乘法、加法到乘法、倒数和Sigmoid函数，其中高效率和高精度由基于实数域的简单计算流程和少量固定通信轮次保证。 我们通过维度变换和蒙特卡洛方法提供了安全且鲁棒的异常检测。 EVA-S2PLoR在精度方面优于许多先进的框架（与大多数框架相比，Sigmoid函数的性能提高了约10个数量级），并在安全逻辑回归实验中表现出最佳的整体性能。 显示英文摘要

摘要： Accurate nonlinear computation is a key challenge in privacy-preserving machine learning (PPML). Most existing frameworks approximate it through linear operations, resulting in significant precision loss. This paper proposes an efficient, verifiable and accurate security 2-party logistic regression framework (EVA-S2PLoR), which achieves accurate nonlinear function computation through a novel secure element-wise multiplication protocol and its derived protocols. Our framework primarily includes secure 2-party vector element-wise multiplication, addition to multiplication, reciprocal, and sigmoid function based on data disguising technology, where high efficiency and accuracy are guaranteed by the simple computation flow based on the real number domain and the few number of fixed communication rounds. We provide secure and robust anomaly detection through dimension transformation and Monte Carlo methods. EVA-S2PLoR outperforms many advanced frameworks in terms of precision (improving the performance of the sigmoid function by about 10 orders of magnitude compared to most frameworks) and delivers the best overall performance in secure logistic regression experiments.