Efficient multi-party secure matrix multiplication is crucial for privacy-preserving machine learning, but existing mixed-protocol frameworks often face challenges in balancing security, efficiency, and accuracy. This paper presents an efficient, verifiable and accurate secure three-party computing (EVA-S3PC) framework that addresses these challenges with elementary 2-party and 3-party matrix operations based on data obfuscation techniques. We propose basic protocols for secure matrix multiplication, inversion, and hybrid multiplication, ensuring privacy and result verifiability. Experimental results demonstrate that EVA-S3PC achieves up to 14 significant decimal digits of precision in Float64 calculations, while reducing communication overhead by up to $54.8\%$ compared to state of art methods. Furthermore, 3-party regression models trained using EVA-S3PC on vertically partitioned data achieve accuracy nearly identical to plaintext training, which illustrates its potential in scalable, efficient, and accurate solution for secure collaborative modeling across domains.

翻译：高效的多方安全矩阵乘法对于隐私保护机器学习至关重要，但现有的混合协议框架往往在平衡安全性、效率与准确性方面面临挑战。本文提出了一种高效、可验证且精确的安全三方计算（EVA-S3PC）框架，该框架基于数据混淆技术，通过基础的2方和3方矩阵运算来解决这些挑战。我们提出了用于安全矩阵乘法、求逆及混合乘法的基本协议，确保了隐私性与结果可验证性。实验结果表明，EVA-S3PC在Float64计算中实现了高达14位有效十进制数字的精度，同时与现有先进方法相比，通信开销降低了$54.8\%$。此外，使用EVA-S3PC在纵向分区数据上训练的三方回归模型，其准确率与明文训练几乎相同，这展示了其在跨领域安全协同建模中提供可扩展、高效且精确解决方案的潜力。