Research map / Federated learning and privacy / Differential privacy
Differentially Private Approximate Quantiles
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 recursive quantile splitting instead of computing each quantile independently | DP quantile estimation · Six datasets, m=1–120 uniform quantiles | average quantile gap | Helped |
| Use recursive quantile splitting under zCDP | DP quantile estimation · Six datasets, m=1–120 uniform quantiles, ρ=1/2 | average quantile gap | Helped |
| Use AQ instead of AggTree | DP quantile estimation · Six datasets, m=1–120 uniform quantiles | runtime | Helped |
| Use AQ instead of JointExp or AppindExp | DP quantile estimation · Six datasets, m=1–120 uniform quantiles | runtime | Helped |
| Use AQ rather than JointExp for small numbers of quantiles | DP quantile estimation · A few small values of m, ε=1 | average quantile gap | Mixed |
Closest papers by meaning
- Differentially Private Quantiles with Smaller Error (NeurIPS 2025 · similarity 0.95)
- Differentially Private Quantiles (ICML 2021 · similarity 0.93)
- Lightweight Protocols for Distributed Private Quantile Estimation (ICML 2025 · similarity 0.90)
- Differentially Private Equivalence Testing for Continuous Distributions and Applications (NeurIPS 2024 · similarity 0.85)
- Private Statistical Estimation via Truncation (NeurIPS 2025 · similarity 0.84)
- A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input (NeurIPS 2025 · similarity 0.84)
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.