Research map / Federated learning and privacy / Differential privacy
Private Statistical Estimation of Many Quantiles
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RecExp instead of IndExp to estimate multiple quantiles | Private statistical quantile estimation · Beta distributions; increasing number of quantiles | quantile estimation error | Helped |
| Use a private histogram density estimator instead of RecExp | Private statistical quantile estimation · Beta(0.5, 0.5); m above approximately 10 | quantile estimation error | Helped |
| Use a private histogram density estimator instead of RecExp | Private statistical quantile estimation · Beta(2, 5); m above approximately 40 | quantile estimation error | Helped |
| Use a private histogram density estimator on distributions with smaller density Lipschitz constants | Private quantile function estimation · Beta distributions | quantile estimation error | Helped |
Closest papers by meaning
- Lightweight Protocols for Distributed Private Quantile Estimation (ICML 2025 · similarity 0.89)
- Differentially Private Quantiles with Smaller Error (NeurIPS 2025 · similarity 0.88)
- Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy (ICML 2024 · similarity 0.87)
- Differentially Private Boxplots (ICML 2025 · similarity 0.85)
- Private Statistical Estimation via Truncation (NeurIPS 2025 · similarity 0.85)
- On Differentially Private U Statistics (NeurIPS 2024 · similarity 0.85)
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