Securely and Efficiently Outsourcing Neural Network Inference via Parallel MSB Extraction
Xin Liu, Ning Xi, Ke Cheng, Jiaxuan Fu, Xinghui Zhu, Yulong Shen, Jianfeng Ma · 2024
Outsourcing neural network (NN) inference services to the cloud gives rise to considerable privacy concerns about the model provider’s proprietary model and the user’s private data. Current cryptography-based secure NN inference schemes are not suited for high-latency networks due to their numerous communication overhead for computing the non-linear components of neural networks. In this paper, we present ParaNN, a secure cloud-based outsourced computation framework that supports lightweight secure neural network inference. At the core of ParaNN, we design a secure and parallel method for extracting the most significant bit (MSB) based on a parallel prefix adder. This forms the cornerstone for a series of secure and communication-efficient computation protocols specifically tailored to non-linear layers like ReLU and Maxpool. Our experiments show that ParaNN achieves a 6.7×-27.4× improvement in online inference time over wide area networks (WAN) compared to the state-of-the-art works.