标题： 基于辅助者协助的恶意安全不诚实多数MPC的高效隐私推理 显示英文标题

标题： Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

摘要： 基于安全多方计算（MPC）的私有推理解决了机器学习即服务（MLaaS）中的数据隐私风险。 然而，现有的基于MPC的私有推理框架专注于半诚实或诚实多数模型，其威胁模型过于理想化，而恶意安全不诚实多数模型面临效率低下的挑战。 为了平衡安全性和效率，我们提出了一种使用辅助者协助恶意安全不诚实多数模型（HA-MSDM）的私有推理框架。 该框架包括我们设计的五种MPC协议和一种协同优化策略。 这些协议实现了高效的固定轮次乘法、指数运算和多项式操作，为私有推理提供了基础原语。 协同优化策略平衡了推理效率和准确性。 为了提高效率，我们对非线性层采用多项式近似。 为了提高准确性，我们在固定区间内构建六阶多项式近似以实现高精度激活函数拟合，并引入参数调整的批量归一化层以限制激活逃逸问题。 在LeNet和AlexNet上的基准结果表明，与最先进的框架（IEEE S&P'25）相比，我们的框架在局域网中实现了2.4-25.7倍的速度提升，在广域网中实现了1.3-9.5倍的加速，仅具有0.04%-1.08%的相对误差，同时保持高准确性。 显示英文摘要

摘要： Private inference based on Secure Multi-Party Computation (MPC) addresses data privacy risks in Machine Learning as a Service (MLaaS). However, existing MPC-based private inference frameworks focuses on semi-honest or honest majority models, whose threat models are overly idealistic, while malicious security dishonest majority models face the challenge of low efficiency. To balance security and efficiency, we propose a private inference framework using Helper-Assisted Malicious Security Dishonest Majority Model (HA-MSDM). This framework includes our designed five MPC protocols and a co-optimized strategy. These protocols achieve efficient fixed-round multiplication, exponentiation, and polynomial operations, providing foundational primitives for private inference. The co-optimized strategy balances inference efficiency and accuracy. To enhance efficiency, we employ polynomial approximation for nonlinear layers. For improved accuracy, we construct sixth-order polynomial approximation within a fixed interval to achieve high-precision activation function fitting and introduce parameter-adjusted batch normalization layers to constrain the activation escape problem. Benchmark results on LeNet and AlexNet show our framework achieves 2.4-25.7x speedup in LAN and 1.3-9.5x acceleration in WAN compared to state-of-the-art frameworks (IEEE S&P'25), maintaining high accuracy with only 0.04%-1.08% relative errors.