  In this paper, we propose a new secure machine learning inference platform
assisted by a small dedicated security processor, which will be easier to
protect and deploy compared to today's TEEs integrated into high-performance
processors. Our platform provides three main advantages over the
state-of-the-art:
  (i) We achieve significant performance improvements compared to
state-of-the-art distributed Privacy-Preserving Machine Learning (PPML)
protocols, with only a small security processor that is comparable to a
discrete security chip such as the Trusted Platform Module (TPM) or on-chip
security subsystems in SoCs similar to the Apple enclave processor. In the
semi-honest setting with WAN/GPU, our scheme is 4X-63X faster than Falcon
(PoPETs'21) and AriaNN (PoPETs'22) and 3.8X-12X more communication efficient.
We achieve even higher performance improvements in the malicious setting.
  (ii) Our platform guarantees security with abort against malicious
adversaries under honest majority assumption.
  (iii) Our technique is not limited by the size of secure memory in a TEE and
can support high-capacity modern neural networks like ResNet18 and Transformer.
  While previous work investigated the use of high-performance TEEs in PPML,
this work represents the first to show that even tiny secure hardware with
really limited performance can be leveraged to significantly speed-up
distributed PPML protocols if the protocol can be carefully designed for
lightweight trusted hardware.
