We design differentially private algorithms for the problem of prediction with expert advice under dynamic regret, also known as tracking the best expert. Our work addresses three natural types of adversaries, stochastic with shifting distributions, oblivious, and adaptive, and designs algorithms with sub-linear regret for all three cases. In particular, under a shifting stochastic adversary where the distribution may shift SSS times, we provide an ε\varepsilonε-differentially private algorithm whose expected dynamic regret is at most O(STlog(NT)+Slog(NT)ε)O\left( \sqrt{S T \log (NT)} +…
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