Private Learning Implies Online Learning: An Efficient Reduction

05/27/2019
by   Alon Gonen, et al.
0

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 NeelAaronRoth2018 asks whether this implication is 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/13/2019

Differentially Private Learning of Geometric Concepts

We present differentially private efficient algorithms for learning unio...
research
07/11/2020

A Computational Separation between Private Learning and Online Learning

A recent line of work has shown a qualitative equivalence between differ...
research
10/10/2022

Do you pay for Privacy in Online learning?

Online learning, in the mistake bound model, is one of the most fundamen...
research
02/09/2019

Passing Tests without Memorizing: Two Models for Fooling Discriminators

We introduce two mathematical frameworks for foolability in the context ...
research
10/30/2018

Private Algorithms Can be Always Extended

We consider the following fundamental question on ϵ-differential privacy...
research
12/09/2021

Differentially Private Ensemble Classifiers for Data Streams

Learning from continuous data streams via classification/regression is p...
research
02/27/2023

On Differentially Private Online Predictions

In this work we introduce an interactive variant of joint differential p...

Please sign up or login with your details

Forgot password? Click here to reset