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SecDD: Efficient and Secure Method for Remotely Training Neural Networks [article]

Ilia Sucholutsky, Matthias Schonlau
2020 arXiv   pre-print
, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels.  ...  We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice  ...  Conclusion and future work We have proposed a method for producing synthetic data that can be used to securely and efficiently train remotely deployed neural networks over unsecured channels.  ... 
arXiv:2009.09155v1 fatcat:6hnes7wpyzgvzjsclmc3z6tine