SoK: Machine Learning with Confidential Computing. (arXiv:2208.10134v1 [cs.CR])

Privacy and security challenges in Machine Learning (ML) have become a
critical topic to address, along with ML's pervasive development and the recent
demonstration of large attack surfaces. As a mature system-oriented approach,
confidential computing has been increasingly utilized in both academia and
industry to improve privacy and security in various ML scenarios. In this
paper, we systematize the findings on confidential computing-assisted ML
security and privacy techniques for providing i) confidentiality guarantees and
ii) integrity assurances. We further identify …

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