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

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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Bibliographic Details
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

Internet

http://link.springer.com/article/10.1186/s13660-016-1280-0

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