2019
NIPS
NeurIPS 2019
Private Learning Implies Online Learning: An Efficient Reduction
Abstract
We study the relationship between the notions of differentially private learning and online learning. 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.
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization and Security & Privacy
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Keyword Pioneer
— efficient reduction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Application Areas > Privacy
Mathematics & Optimization > Optimization > Online Algorithms
Machine Learning > Learning Types > Online Learning
Machine Learning > Optimization & Theory > Online Algorithms
Security & Privacy > Differential Privacy
Machine Learning > Learning Types > Privacy