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SoK: Privacy-preserving Deep Learning with Homomorphic Encryption. (arXiv:2112.12855v2 [cs.CR] UPDATED)
Jan. 4, 2022, 2:10 a.m. | Robert Podschwadt, Daniel Takabi, Peizhao Hu
cs.LG updates on arXiv.org arxiv.org
Outsourced computation for neural networks allows users access to state of
the art models without needing to invest in specialized hardware and know-how.
The problem is that the users lose control over potentially privacy sensitive
data. With homomorphic encryption (HE) computation can be performed on
encrypted data without revealing its content. In this systematization of
knowledge, we take an in-depth look at approaches that combine neural networks
with HE for privacy preservation. We categorize the changes to neural network
models …
More from arxiv.org / cs.LG updates on arXiv.org
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