Summary of Resampling Methods For Private Statistical Inference, by Karan Chadha et al.
Resampling methods for private statistical inference
by Karan Chadha, John Duchi, Rohith Kuditipudi
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Methodology (stat.ME)
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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 proposed paper explores the construction of confidence intervals with differential privacy for non-parametric bootstrap methods. Two private variants are introduced, which privately compute the median of results from multiple “little” bootstraps on data partitions and provide asymptotic bounds on coverage error. For a fixed parameter, these methods achieve similar error rates to the non-private bootstrap within logarithmic factors in sample size n. Empirical validation is performed using real and synthetic datasets for mean estimation, median estimation, and logistic regression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates confidence intervals with private data protection. It offers two ways to do this: by bootstrapping small parts of the data many times or by combining different methods together. The results show that these new methods work as well as older ones but take much less time to compute, which is useful for big datasets. |
Keywords
* Artificial intelligence * Bootstrapping * Logistic regression
