  Privacy and security-related concerns are growing as machine learning reaches
diverse application domains. The data holders want to train with private data
while exploiting accelerators, such as GPUs, that are hosted in the cloud.
However, Cloud systems are vulnerable to attackers that compromise the privacy
of data and integrity of computations. This work presents DarKnight, a
framework for large DNN training while protecting input privacy and computation
integrity. DarKnight relies on cooperative execution between trusted execution
environments (TEE) and accelerators, where the TEE provides privacy and
integrity verification, while accelerators perform the computation heavy linear
algebraic operations.
