Do you pay for Privacy in Online learning?
Amartya Sanyal, Giorgia Ramponi

TL;DR
This paper investigates whether online learning can inherently provide differential privacy without additional mechanisms, exploring the potential equivalence between online learning and privacy guarantees.
Contribution
It formalizes the question of whether privacy can be achieved at no extra cost in online learning, bridging a gap between learning theory and privacy.
Findings
Addresses the theoretical relationship between online learning and differential privacy
Raises the question of whether privacy is achievable without additional effort in online settings
Provides a foundation for future research on privacy-preserving online algorithms
Abstract
Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. 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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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Adversarial Robustness in Machine Learning · Stochastic Gradient Optimization Techniques
