@inproceedings{c26eb26d76d14e8aadc8f4289144a31b,
title = "An equivalence between private classification and online prediction",
abstract = "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.",
keywords = "differential privacy, learning, online learning",
author = "Mark Bun and Roi Livni and Shay Moran",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020 ; Conference date: 16-11-2020 Through 19-11-2020",
year = "2020",
month = nov,
doi = "10.1109/FOCS46700.2020.00044",
language = "אנגלית",
series = "Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS",
publisher = "IEEE Computer Society",
pages = "389--402",
booktitle = "Proceedings - 2020 IEEE 61st Annual Symposium on Foundations of Computer Science, FOCS 2020",
address = "ארצות הברית",
}