  We study the relationship between the notions of differentially private
learning and online learning in games. Several recent works have shown that
differentially private learning implies online learning, but an open problem of
Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is
{\it efficient}. Specifically, does an efficient differentially private learner
imply an efficient online learner? In this paper we resolve this open question
in the context of pure differential privacy. We derive an efficient black-box
reduction from differentially private learning to online learning from expert
advice.
