Defending Side-Channel Attacks in Convolutional Neural Networks with Channel-Level Parallelization
Yankun Zhu, Ranxi Lin, Pingqiang Zhou · 2025
Side-channel attacks (SCAs) pose significant threats to the security of neural networks (NNs) deployed on hardware platforms, especially in cloud Field-Programmable Gate Array (FPGA) environments. This paper presents a novel approach to enhance the security of convolutional layers in NNs against SCAs by introducing a channel-level parallel structure. Compared with the original structure and the state-of-the-art masking technique, the channel-level parallel structure significantly reduces the success rate of SCAs (from 97.13% to 5.46% on average) and is able to be optimized for either low resource overhead (83.64% reduction) or good timing performance (83.01% improvement).