DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware. (arXiv:2207.00083v1 [cs.CR])

Privacy and security-related concerns are growing as machine learning reaches
diverse application domains. The data holders want to train or infer with
private data while exploiting accelerators, such as GPUs, that are hosted in
the cloud. Cloud systems are vulnerable to attackers that compromise the
privacy of data and integrity of computations. Tackling such a challenge
requires unifying theoretical privacy algorithms with hardware security
capabilities. This paper presents DarKnight, a framework for large DNN training
while protecting input privacy and …

deep learning framework hardware integrity privacy trusted hardware