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CrypTen: Secure Multi-Party Computation Meets Machine Learning. (arXiv:2109.00984v2 [cs.LG] UPDATED)
Sept. 19, 2022, 1:20 a.m. | Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten
cs.CR updates on arXiv.org arxiv.org
Secure multi-party computation (MPC) allows parties to perform computations
on data while keeping that data private. This capability has great potential
for machine-learning applications: it facilitates training of machine-learning
models on private data sets owned by different parties, evaluation of one
party's private model using another party's private data, etc. Although a range
of studies implement machine-learning models via secure MPC, such
implementations are not yet mainstream. Adoption of secure MPC is hampered by
the absence of flexible software frameworks …
More from arxiv.org / cs.CR updates on arXiv.org
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