Private Learning Implies Online Learning: An Efficient Reduction
Alon Gonen, Elad Hazan, Shay Moran

TL;DR
This paper establishes an efficient black-box reduction from differentially private learning to online learning, resolving an open question about the implications of privacy-preserving algorithms in online settings.
Contribution
It provides the first efficient reduction showing that differentially private learning implies online learning from expert advice.
Findings
Efficient reduction from private learning to online learning.
Addresses an open problem in the theory of private and online learning.
Clarifies the relationship between privacy and online learning efficiency.
Abstract
We study the relationship between the notions of differentially private learning and online learning in games. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}. Specifically, does an efficient differentially private learner imply an efficient online learner? In this paper we resolve this open question in the context of pure differential privacy. We derive an efficient black-box reduction from differentially private learning to online learning from expert advice.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Complexity and Algorithms in Graphs
