Towards Zero Rotation and Beyond: Architecting Neural Networks for Fast Secure Inference with Homomorphic Encryption
Hongyi Wu
Privacy-preserving deep learning addresses privacy concerns in Machine Learning as a Service (MLaaS) using Homomorphic Encryption (HE) for linear computations. Nevertheless, the high computational cost remains a challenge. While prior work has attempted to improve the efficiency, most are built upon models originally designed for plaintext inference. These models are inherently limited by architectural inefficiencies when adapted to HE settings. We argue that substantial efficiency improvements