Research map / Federated learning and privacy / Differential privacy
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Noise Reduction with correlated noise and InteractiveAboveThreshold instead of DoublingMethod | Covariance Perturbation · Twitter ridge regression, α=0.05 | privacy risk factor e^ε: 495 -> 10.0 | Helped |
| Use Noise Reduction with correlated noise and InteractiveAboveThreshold instead of DoublingMethod | Covariance Perturbation · Twitter ridge regression, α=0.075 | privacy risk factor e^ε: 56.6 -> 4.65 | Helped |
Closest papers by meaning
- Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints (NeurIPS 2022 · similarity 0.89)
- Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases (NeurIPS 2019 · similarity 0.89)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.89)
- Auditing $f$-differential privacy in one run (ICML 2025 · similarity 0.89)
- Purifying Approximate Differential Privacy with Randomized Post-processing (NeurIPS 2025 · similarity 0.88)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.