Data-Oblivious ML Accelerators using Hardware Security Extensions. (arXiv:2401.16583v1 [cs.CR])

Outsourced computation can put client data confidentiality at risk. Existing
solutions are either inefficient or insufficiently secure: cryptographic
techniques like fully-homomorphic encryption incur significant overheads, even
with hardware assistance, while the complexity of hardware-assisted trusted
execution environments has been exploited to leak secret data.

Recent proposals such as BliMe and OISA show how dynamic information flow
tracking (DIFT) enforced in hardware can protect client data efficiently. They
are designed to protect CPU-only workloads. However, many outsourced computing
applications, like machine …

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