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Register a new userScalable Differential Privacy with Certified Robustness in Adversarial Learning
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Added by
NhatHai Phan
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples. By leveraging the sequential composition theory in DP, we randomize both input and latent spaces to strengthen our certified robustness bounds. To address the trade-off among model utility, privacy loss, and robustness, we design an original adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model. A new stochastic batch training is proposed to apply our mechanism on large DNNs and datasets, by bypassing the vanilla iterative batch-by-batch training in DP DNNs. An end-to-end theoretical analysis and evaluations show that our mechanism notably improves the robustness and scalability of DP DNNs.
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Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Alas, recent work highlighted several privacy and robustness weaknesses in FL, presenting, respectively, membership/property inference and backdoor attacks. In this paper, we investigate to what extent Differential Privacy (DP) can be used to protect not only privacy but also robustness in FL. We present a first-of-its-kind empirical evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness. We show that both DP variants do defend against backdoor attacks, with varying levels of protection and utility, and overall much more effectively than previously proposed defenses. They also mitigate white-box membership inference attacks in FL, and our work is the first to show how effectively; neither, however, provides viable defenses against property inference. Our work also provides a re-usable measurement framework to quantify the trade-offs between robustness/privacy and utility in differentially private FL.
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Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against patch attacks. We begin with a set of experiments showing that most existing defenses, which work by pre-processing input images to mitigate adversarial patches, are easily broken by simple white-box adversaries. Motivated by this finding, we propose the first certified defense against patch attacks, and propose faster methods for its training. Furthermore, we experiment with different patch shapes for testing, obtaining surprisingly good robustness transfer across shapes, and present preliminary results on certified defense against sparse attacks. Our complete implementation can be found on: https://github.com/Ping-C/certifiedpatchdefense.
This short note highlights some links between two lines of research within the emerging topic of trustworthy machine learning: differential privacy and robustness to adversarial examples. By abstracting the definitions of both notions, we show that they build upon the same theoretical ground and hence results obtained so far in one domain can be transferred to the other. More precisely, our analysis is based on two key elements: probabilistic mappings (also called randomized algorithms in the differential privacy community), and the Renyi divergence which subsumes a large family of divergences. We first generalize the definition of robustness against adversarial examples to encompass probabilistic mappings. Then we observe that Renyi-differential privacy (a generalization of differential privacy recently proposed in~cite{Mironov2017RenyiDP}) and our definition of robustness share several similarities. We finally discuss how can both communities benefit from this connection to transfer technical tools from one research field to the other.
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