Research map / Federated learning and privacy / Differential privacy
Regression with Label Differential Privacy
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Laplace label noise with randomized response on bins (optimal regression mechanism) | label DP regression on Criteo · Criteo Sponsored Search Conversion | MSE (Mechanism) and test MSE: ~1.5x smaller test MSE at eps=0.5 vs best baseline (paper's claim) | Helped |
| Use RR-on-Bins with privately estimated prior (two-step) instead of Laplace noise | label DP regression · Criteo Sponsored Search Conversion | MSE (Generalization) | Helped |
Closest papers by meaning
- Enhancing Learning with Label Differential Privacy by Vector Approximation (ICLR 2025 · similarity 0.90)
- Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference (ICML 2025 · similarity 0.90)
- Differentially Private Model Merging (NeurIPS 2026 · similarity 0.87)
- Optimal Differentially Private Model Training with Public Data (ICML 2024 · similarity 0.87)
- Certification for Differentially Private Prediction in Gradient-Based Training (ICML 2025 · similarity 0.86)
- It's My Data Too: Private ML for Datasets with Multi-User Training Examples (ICML 2025 · similarity 0.86)
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