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On Statistical Robustness and Differential Privacy : = Theory and Algorithms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On Statistical Robustness and Differential Privacy :/
其他題名:	Theory and Algorithms.
作者:	Liu, Zheng.
面頁冊數:	1 online resource (162 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29326792click for full text (PQDT)
ISBN:	9798841759942

On Statistical Robustness and Differential Privacy : = Theory and Algorithms.
Liu, Zheng.

On Statistical Robustness and Differential Privacy :Theory and Algorithms. - 1 online resource (162 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2022.

Includes bibliographical references

We study the statistical robustness and differential privacy, providing theories and algorithms. We first investigate the vulnerability of deep learning algorithms in the context of medical image segmentation and propose methods that may be adopted during training to make deep learning algorithms more robust. We show attack strategies based on Fast Gradient Sign Method (FGSM) can generate visually subtle perturbations to input images which severely affect segmentation result. We also show defense strategies, based on adversarial training and defensive distillation, show significantly improved robustness with respect to adversarial attacks. We then study robust estimation, where we propose Wasserstein contamination model and a W-GAN based robust estimator. We then present a general technique for upper-bounding the minimax rate of W-GAN-based estimators, and derive a general lower bound on the minimax rate via the modulus of continuity. The upper and lower bounds match in many estimation settings of interest, indicating that the W-GAN-based estimator achieves the optimal minimax rate.In the field of differential privacy, we study the differentially private estimation and synthetic dataset construction. For differentially private synthetic dataset construction, we propose a mechanism to efficiently release a synthetic dataset which could be used for various downstream applications. We give an Maximum Mean Discrepancy (MMD) bound between raw and released dataset. We also show this bound could be readily transformed into a bound for downstream analysis, e.g. principal component analysis (PCA), discriminant analysis and clustering, enabling the wide application of the mechanism. Then we consider differentially private estimation. We study the high dimensional linear regression model, using regularized M-estimator. We propose a differentially private regularized m-estimator via noisy composite gradient descent and show upper bound on the convergence rate of the private estimator.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841759942Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Statistical robustnessIndex Terms--Genre/Form:

542853
Electronic books.

On Statistical Robustness and Differential Privacy : = Theory and Algorithms.
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504 $a Includes bibliographical references
520 $a We study the statistical robustness and differential privacy, providing theories and algorithms. We first investigate the vulnerability of deep learning algorithms in the context of medical image segmentation and propose methods that may be adopted during training to make deep learning algorithms more robust. We show attack strategies based on Fast Gradient Sign Method (FGSM) can generate visually subtle perturbations to input images which severely affect segmentation result. We also show defense strategies, based on adversarial training and defensive distillation, show significantly improved robustness with respect to adversarial attacks. We then study robust estimation, where we propose Wasserstein contamination model and a W-GAN based robust estimator. We then present a general technique for upper-bounding the minimax rate of W-GAN-based estimators, and derive a general lower bound on the minimax rate via the modulus of continuity. The upper and lower bounds match in many estimation settings of interest, indicating that the W-GAN-based estimator achieves the optimal minimax rate.In the field of differential privacy, we study the differentially private estimation and synthetic dataset construction. For differentially private synthetic dataset construction, we propose a mechanism to efficiently release a synthetic dataset which could be used for various downstream applications. We give an Maximum Mean Discrepancy (MMD) bound between raw and released dataset. We also show this bound could be readily transformed into a bound for downstream analysis, e.g. principal component analysis (PCA), discriminant analysis and clustering, enabling the wide application of the mechanism. Then we consider differentially private estimation. We study the high dimensional linear regression model, using regularized M-estimator. We propose a differentially private regularized m-estimator via noisy composite gradient descent and show upper bound on the convergence rate of the private estimator.
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