Private and Online Learnability Are Equivalent

Noga Alon; Mark Bun; Roi Livni; Maryanthe Malliaris; Shay Moran

doi:10.1145/3526074

Private and Online Learnability Are Equivalent

Noga Alon, Mark Bun, Roi Livni, Maryanthe Malliaris, Shay Moran

Mathematics

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

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.

Original language	English (US)
Article number	28
Journal	Journal of the ACM
Volume	69
Issue number	4
DOIs	https://doi.org/10.1145/3526074
State	Published - Aug 16 2022

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Information Systems
Hardware and Architecture
Artificial Intelligence

Keywords

Differential privacy
Littlestone dimension
PAC learning
online learning

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10.1145/3526074

Cite this

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title = "Private and Online Learnability Are Equivalent",

abstract = "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.",

keywords = "Differential privacy, Littlestone dimension, PAC learning, online learning",

author = "Noga Alon and Mark Bun and Roi Livni and Maryanthe Malliaris and Shay Moran",

note = "Publisher Copyright: {\textcopyright} 2022 Association for Computing Machinery.",

year = "2022",

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doi = "10.1145/3526074",

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T1 - Private and Online Learnability Are Equivalent

AU - Alon, Noga

AU - Bun, Mark

AU - Livni, Roi

AU - Malliaris, Maryanthe

AU - Moran, Shay

PY - 2022/8/16

Y1 - 2022/8/16

N2 - 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.

AB - 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.

KW - Differential privacy

KW - Littlestone dimension

KW - PAC learning

KW - online learning

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Private and Online Learnability Are Equivalent

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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