Multiclass versus Binary Differentially Private PAC Learning

Part of Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)


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Satchit Sivakumar, Mark Bun, Marco Gaboardi


We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning. We apply this transformation to a recently proposed binary private PAC learner to obtain a private multiclass learner with sample complexity that has a polynomial dependence on the multiclass Littlestone dimension and a poly-logarithmic dependence on the number of classes. This yields a doubly exponential improvement in the dependence on both parameters over learners from previous work. Our proof extends the notion of $\Psi$-dimension defined in work of Ben-David et al. [JCSS, 1995] to the online setting and explores its general properties.