HWGN2: Side-channel Protected Neural Networks through Secure and Private Function Evaluation. (arXiv:2208.03806v1 [cs.CR])

Recent work has highlighted the risks of intellectual property (IP) piracy of
deep learning (DL) models from the side-channel leakage of DL hardware
accelerators. In response, to provide side-channel leakage resiliency to DL
hardware accelerators, several approaches have been proposed, mainly borrowed
from the methodologies devised for cryptographic implementations. Therefore, as
expected, the same challenges posed by the complex design of such
countermeasures should be dealt with. This is despite the fact that fundamental
cryptographic approaches, specifically secure and private …

channel networks neural networks side-channel