  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, 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.
