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 [27] 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.
Original language | English |
---|---|
Journal | Advances in Neural Information Processing Systems |
Volume | 32 |
State | Published - 2019 |
Externally published | Yes |
Event | 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 |
All Science Journal Classification (ASJC) codes
- Information Systems
- Signal Processing
- Computer Networks and Communications