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Online learning, in the mistake bound model, is one of the most fundamentalconcepts in learning theory. Differential privacy, instead, is the most widelyused statistical concept of privacy in the machine learning community. It isthus clear that defining learning problems that are online differentiallyprivately learnable is of great interest. In this paper, we pose the questionon if the two problems are equivalent from a learning perspective, i.e., isprivacy for free in the online learning framework?

Do you pay for Privacy in Online learning?