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Confidential Inference via Ternary Model Partitioning
[article]
2020
arXiv
pre-print
Today's cloud vendors are competing to provide various offerings to simplify and accelerate AI service deployment. However, cloud users always have concerns about the confidentiality of their runtime data, which are supposed to be processed on third-party's compute infrastructures. Information disclosure of user-supplied data may jeopardize users' privacy and breach increasingly stringent data protection regulations. In this paper, we systematically investigate the life cycles of inference
arXiv:1807.00969v3
fatcat:y5fxdsexh5dwdklaqg62gj5hxy