QUOTIENT: two-party secure neural network training and prediction
Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs are trained on data, a key question is how such models can be also...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Association for Computing Machinery
2019
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