Summary and Contributions: Update following the the author response: I thank the authors for the clarifications. This paper explores the connection between learning with approximate privacy and online learning for multi-class and regression problems. It follows prior work that showed an equivalence between approximate privacy and online learning for binary classification. This paper has two main results: (1) Multi-class: Consider a hypothesis class H of functions from X to Y where Y is finite. Then, H is PAC learnable with respect to the 0-1 loss in the online setting with if and only if it is learnable with approximate privacy in the standard stochastic batch setting.