Summary of Notes on Sampled Gaussian Mechanism, by Nikita P. Kalinin
Notes on Sampled Gaussian Mechanism
by Nikita P. Kalinin
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The recent conjecture by Räisä et al. (2024) is resolved through a rigorous proof of Theorem 6.2, which states that the effective noise level in the Sampled Gaussian Mechanism decreases with increasing subsampling rates. This mechanism, composed of subsampling and additive Gaussian noise, is used for differentially private stochastic optimization. The result implies that larger subsampling rates are preferred to achieve better privacy-utility trade-offs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large batch sizes work well for differentially private stochastic optimization when using the Sampled Gaussian Mechanism. A recent conjecture proved that the effective noise level decreases as subsampling rate increases, making it beneficial to use larger subsampling rates for better privacy and utility. |
Keywords
» Artificial intelligence » Optimization
