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  1. ; ; ; ; ( , Journal of the ACM)
    Let H be a binary-labeled concept class. We prove that H can be PAC learned by an (approximate) differentially private algorithm if and only if it has a finite Littlestone dimension. This implies a qualitative equivalence between online learnability and private PAC learnability. 
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    Full Text Available 
  2. ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  3. ; ; ( , 61st Annual IEEE Symposium on Foundations of Computer Science)
    null (Ed.)
    We prove that every concept class with finite Littlestone dimension can be learned by an (approximate) differentially-private algorithm. This answers an open question of Alon et al. (STOC 2019) who proved the converse statement (this question was also asked by Neel et al. (FOCS 2019)). Together these two results yield an equivalence between online learnability and private PAC learnability. We introduce a new notion of algorithmic stability called “global stability” which is essential to our proof and may be of independent interest. We also discuss an application of our results to boosting the privacy and accuracy parameters of differentially-private learners. 
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    Accepted Manuscript Full Text Available