Closure Properties for Private Classification and Online Prediction

Let~cH be a class of boolean functions and consider a it composed class cH' that is derived from~cH using some arbitrary aggregation rule (for example, cH' may be the class of all 3-wise majority-votes of functions in cH). We upper bound the Littlestone dimension of~cH' in terms of that of~cH. As a corollary, we derive closure properties for online learning and private PAC learning. The derived bounds on the Littlestone dimension exhibit an undesirable exponential dependence. For private learning, we prove close to optimal bounds that circumvents this suboptimal dependency. The improved bounds on the sample complexity of private learning are derived algorithmically via transforming a private learner for the original class cH to a private learner for the composed class~cH'. Using the same ideas we show that any (em proper or improper) private algorithm that learns a class of functions cH in the realizable case (i.e., when the examples are labeled by some function in the class) can be transformed to a private algorithm that learns the class cH in the agnostic case.