A computational separation between private learning and online learning

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2007.05665.pdf(205.08 KB)
Accepted manuscript
Date
2020-12-06
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Authors
Bun, Mark
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OA Version
Citation
M. Bun. 2020. "A computational separation between private learning and online learning." Neural Information Processing Systems (NeurIPS) https://arxiv.org/abs/2007.05665
Abstract
A recent line of work has shown a qualitative equivalence between differentially private PAC learning and online learning: A concept class is privately learnable if and only if it is online learnable with a finite mistake bound. However, both directions of this equivalence incur significant losses in both sample and computational efficiency. Studying a special case of this connection, Gonen, Hazan, and Moran (NeurIPS 2019) showed that uniform or highly sample-efficient pure-private learners can be time-efficiently compiled into online learners. We show that, assuming the existence of one-way functions, such an efficient conversion is impossible even for general pure-private learners with polynomial sample complexity. This resolves a question of Neel, Roth, and Wu (FOCS 2019).
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