Private Online Learning via Lazy Algorithms

We study the problem of private online learning, focusing on online prediction from experts (OPE) and online convex optimization (OCO). We propose a new transformation that translates lazy, low-switching online learning algorithms into private algorithms. We apply our transformation to differentially private OPE and OCO using existing lazy algorithms for these problems.