A GPU-Based Privacy-Preserving Machine Learning Acceleration Scheme

As the application of artificial intelligence expands, privacy-preserving machine learning has become a critical research focus. Secret sharing, as a commonly used privacy-preserving technique, has broad application prospects due to its lightweight ciphertext computation characteristics. While secret sharing offers lightweight computation, most schemes are implemented on CPU platforms, leaving room for exploration on GPUs. This paper proposes a GPU-based acceleration scheme for privacy-preserving machine learning, utilizing the ABY3 secret sharing protocol and a 32-bit integer ring, while supporting regularization techniques. Experimental results on standard neural networks, such as VGG-16 and AlexNet, demonstrate a significant performance improvement. The proposed approach achieves a 95× improvement over the CPU-based Falcon scheme and a 3.5× improvement over the GPU-based CryptGPU scheme during privacy training, while reducing communication volume by over 50% in both inference and training phases.