Do you pay for Privacy in Online learning?
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
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Rights / License
Abstract
Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory and differential privacy is, perhaps, the most widely used statistical concept of privacy in the machine learning community. Thus, defining problems which are online differentially privately learnable is of great interest in learning theory. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?
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Publication status
published
Book title
Proceedings of Thirty Fifth Conference on Learning Theory
Journal / series
Volume
178
Pages / Article No.
5633 - 5637
Publisher
PMLR
Event
35th Annual Conference on Learning Theory (COLT 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09729 - He, Niao / He, Niao
09652 - Yang, Fan / Yang, Fan