Projection-Free Bandit Optimization with Privacy Guarantees

We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a complex geometry, and access to it is done efficiently only through a linear optimization oracle, hence Euclidean projections are unavailable (e.g. matroid polytope, submodular base polytope). This is the first differentially-private algorithm for projection-free bandit optimization, and in fact our bound of O(T 3/4 ) matches the best known non-private projection-free algorithm (Garber-Kretzu, AISTATS '20) and the best known private algorithm, even for the weaker setting when projections are available (Smith-Thakurta, NeurIPS '13).