Privacy-Preserving Automated Deep Learning for Secure Inference Service

This research proposes 2PCAutoDL, a system for automatically designing deep neural networks (DNNs, which are AI models with many layers) while keeping data and model designs private by splitting computations between two separate cloud servers. The system balances security and speed by using specialized protocols (step-by-step procedures) for different types of network layers, achieving significant speedups compared to existing approaches while maintaining similar model accuracy.