Differential Privacy in Adversarial Learning with Provable Robustness

ICLR 2020 Anonymous

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples. We leverage the sequential composition theory in DP, to establish a new connection between DP preservation and provable robustness... (read more)

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