Research map / Federated learning and privacy / Differential privacy
Online Local Differential Private Quantile Inference via Self-normalization
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 |
|---|---|---|---|
| Increase truthful response rate from r=0.25 to r=0.9 | Online LDP quantile inference · Normal, n=10000, τ=0.5 | mean absolute error: 0.037 -> 0.011 | Helped |
| Increase truthful response rate from r=0.25 to r=0.9 | Online LDP quantile inference · Normal, n=10000, τ=0.3 | coverage rate: 0.926 -> 0.982 | Mixed |
| Increase sample size | Online LDP quantile inference · Normal, τ=0.5, r=0.5 | coverage rate: 0.897 -> 0.949 | Helped |
| Initialize closer to the true target quantile | Online LDP quantile inference · Normal, early iterations | estimation error | Helped |
Closest papers by meaning
- Lightweight Protocols for Distributed Private Quantile Estimation (ICML 2025 · similarity 0.92)
- Differentially Private Quantiles with Smaller Error (NeurIPS 2025 · similarity 0.89)
- Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy (ICML 2024 · similarity 0.89)
- Locally Optimal Private Sampling: Beyond the Global Minimax (NeurIPS 2025 · similarity 0.88)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.87)
- Minimax Optimal Two-Sample Testing under Local Differential Privacy (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.