Research map / Federated learning and privacy / Differential privacy
Pain-Free Random Differential Privacy with Sensitivity Sampling
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 sampled rather than global sensitivity to calibrate the Laplace mechanism | Linear SVM · Synthetic SVM data | L1 sensitivity: exceeds 20 vs. around 0.01 | Helped |
| Use sampled rather than global sensitivity to calibrate the Laplace mechanism | Linear SVM · Synthetic SVM data, d=2 | misclassification error | Helped |
| Use sampled rather than global sensitivity to calibrate the Bernstein mechanism | Kernel density estimation · One-dimensional KDE mixture | error relative to non-private | Helped |
| Use sampled rather than global sensitivity | Sample mean release · Uniform data on [0,1]^d | sensitivity | Helped |
Closest papers by meaning
- Approximate Differential Privacy of the $\ell_2$ Mechanism (ICML 2025 · similarity 0.90)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.89)
- Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation (NeurIPS 2026 · similarity 0.87)
- Certification for Differentially Private Prediction in Gradient-Based Training (ICML 2025 · similarity 0.87)
- Beyond the Calibration Point: Mechanism Comparison in Differential Privacy (ICML 2024 · similarity 0.87)
- Universal Exact Compression of Differentially Private Mechanisms (NeurIPS 2024 · 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.