Summary of Empirical Mean and Frequency Estimation Under Heterogeneous Privacy: a Worst-case Analysis, by Syomantak Chaudhuri et al.
Empirical Mean and Frequency Estimation Under Heterogeneous Privacy: A Worst-Case Analysis
by Syomantak Chaudhuri, Thomas A. Courtade
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 paper addresses estimation problems under heterogeneous differential privacy (DP) constraints, where each user’s data is subject to a different level of privacy protection. It proposes novel algorithms for empirical mean estimation and frequency estimation in both correlated and uncorrelated settings. The approach achieves optimal performance under PAC error and mean-squared error metrics, outperforming baseline techniques experimentally. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves problems in data analysis that are important to industries, where people want different levels of privacy protection. It finds new ways to estimate things like averages and frequencies while keeping this data private. The algorithms work well in many situations and are better than others at doing the same thing. |
