Perturbation of convex risk minimization and its application in differential private learning algorithms

Abstract Convex risk minimization is a commonly used setting in learning theory. In this paper, we firstly give a perturbation analysis for such algorithms, and then we apply this result to differential private learning algorithms. Our analysis needs the objective functions to be strongly convex. Th...

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Main Authors:	Weilin Nie, Cheng Wang
Format:	Article
Language:	English
Published:	SpringerOpen 2017-01-01
Series:	Journal of Inequalities and Applications
Subjects:	differential privacy convex risk minimization perturbation concentration inequality error decomposition
Online Access:	http://link.springer.com/article/10.1186/s13660-016-1280-0

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spelling	doaj-09cdec8de9694e40b1517767b567728a2020-11-24T23:11:17ZengSpringerOpenJournal of Inequalities and Applications1029-242X2017-01-012017111610.1186/s13660-016-1280-0Perturbation of convex risk minimization and its application in differential private learning algorithmsWeilin Nie0Cheng Wang1Huizhou UniversityHuizhou UniversityAbstract Convex risk minimization is a commonly used setting in learning theory. In this paper, we firstly give a perturbation analysis for such algorithms, and then we apply this result to differential private learning algorithms. Our analysis needs the objective functions to be strongly convex. This leads to an extension of our previous analysis to the non-differentiable loss functions, when constructing differential private algorithms. Finally, an error analysis is then provided to show the selection for the parameters.http://link.springer.com/article/10.1186/s13660-016-1280-0differential privacyconvex risk minimizationperturbationconcentration inequalityerror decomposition
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Weilin Nie Cheng Wang
spellingShingle	Weilin Nie Cheng Wang Perturbation of convex risk minimization and its application in differential private learning algorithms Journal of Inequalities and Applications differential privacy convex risk minimization perturbation concentration inequality error decomposition
author_facet	Weilin Nie Cheng Wang
author_sort	Weilin Nie
title	Perturbation of convex risk minimization and its application in differential private learning algorithms
title_short	Perturbation of convex risk minimization and its application in differential private learning algorithms
title_full	Perturbation of convex risk minimization and its application in differential private learning algorithms
title_fullStr	Perturbation of convex risk minimization and its application in differential private learning algorithms
title_full_unstemmed	Perturbation of convex risk minimization and its application in differential private learning algorithms
title_sort	perturbation of convex risk minimization and its application in differential private learning algorithms
publisher	SpringerOpen
series	Journal of Inequalities and Applications
issn	1029-242X
publishDate	2017-01-01
description	Abstract Convex risk minimization is a commonly used setting in learning theory. In this paper, we firstly give a perturbation analysis for such algorithms, and then we apply this result to differential private learning algorithms. Our analysis needs the objective functions to be strongly convex. This leads to an extension of our previous analysis to the non-differentiable loss functions, when constructing differential private algorithms. Finally, an error analysis is then provided to show the selection for the parameters.
topic	differential privacy convex risk minimization perturbation concentration inequality error decomposition
url	http://link.springer.com/article/10.1186/s13660-016-1280-0
work_keys_str_mv	AT weilinnie perturbationofconvexriskminimizationanditsapplicationindifferentialprivatelearningalgorithms AT chengwang perturbationofconvexriskminimizationanditsapplicationindifferentialprivatelearningalgorithms
_version_	1725604990566793216

Perturbation of convex risk minimization and its application in differential private learning algorithms

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