Binarized Neural Networks (BNN) offer efficient implementations for machine learning tasks and facilitate Privacy-Preserving Machine Learning (PPML) by simplifying operations with binary values. Nevertheless, challenges persist in terms of communication and accuracy in their application scenarios. In this work, we introduce CBNN, a three-party secure computation framework tailored for efficient BNN inference. Leveraging knowledge distillation and separable convolutions, CBNN transforms standard BNNs into MPC-friendly customized BNNs, maintaining high utility. It performs secure inference using optimized protocols for basic operations. Specifically, CBNN enhances linear operations with replicated secret sharing and MPC-friendly convolutions, while introducing a novel secure activation function to optimize non-linear operations. We demonstrate the effectiveness of CBNN by transforming and securely implementing several typical BNN models. Experimental results indicate that CBNN maintains impressive performance even after customized binarization and security measures

翻译：二值神经网络（BNN）为机器学习任务提供了高效实现，并通过简化二值运算促进了隐私保护机器学习（PPML）。然而，在其应用场景中仍存在通信开销与精度方面的挑战。本研究提出CBNN，一个专为高效BNN推理定制的三方安全计算框架。通过知识蒸馏与可分离卷积技术，CBNN将标准BNN转化为适配多方安全计算的定制化BNN，同时保持较高的实用性能。该框架采用针对基础运算的优化协议执行安全推理。具体而言，CBNN通过复制秘密共享和多方安全计算友好型卷积增强线性运算，同时引入创新的安全激活函数以优化非线性运算。我们通过对多个典型BNN模型进行定制化转换与安全实现，验证了CBNN的有效性。实验结果表明，CBNN在经历定制化二值化与安全处理后仍能保持卓越性能。