Summary of Differentially-private Collaborative Online Personalized Mean Estimation, by Yauhen Yakimenka et al.
Differentially-Private Collaborative Online Personalized Mean Estimation
by Yauhen Yakimenka, Chung-Wei Weng, Hsuan-Yin Lin, Eirik Rosnes, Jörg Kliewer
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 research proposes a method for collaborative personalized mean estimation under privacy constraints in a multi-agent environment. The proposed algorithm uses hypothesis testing coupled with differential privacy and data variance estimation, and is shown to provide faster convergence than fully local approaches. Two privacy mechanisms and two data variance estimation schemes are introduced, and the theoretical performance of the algorithm is analyzed. Numerical results demonstrate that the proposed approach converges much faster than a fully local approach, while performing similarly to ideal performance where all data is public. This illustrates the benefits of private collaboration in an online setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem where many agents need to work together to estimate averages of their own data without sharing it with each other. They come up with a new way to do this using statistical tests and privacy rules. The idea is that by working together, they can get the answer faster than if they did it alone. The researchers test this idea and show that it really works. |
