Summary of On the Growth Of Mistakes in Differentially Private Online Learning: a Lower Bound Perspective, by Daniil Dmitriev et al.
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective
by Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper sets a new standard for Differentially Private (DP) Online Learning algorithms by providing lower bounds for these types of algorithms. The authors show that, for a wide range of DP online algorithms, the expected number of mistakes grows logarithmically with the number of rounds T, as long as log T is proportional to 1/δ. This result matches an upper bound established in 2021 and contrasts with non-private online learning where the number of mistakes does not depend on T. The authors’ work is a significant step towards resolving open questions in DP-online learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well algorithms can learn from data while keeping personal information safe. The researchers found that certain types of algorithms make more mistakes as they learn, and this mistake rate grows with the amount of data used. This discovery could help improve online learning methods that prioritize privacy. |
Keywords
* Artificial intelligence * Online learning
