Research map / Federated learning and privacy / Differential privacy
A Central Limit Theorem for Differentially Private Query Answering
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 Gaussian noise in the GDP-parameterized noise-addition mechanism | High-dimensional query-answering noise-addition mechanisms · High-dimensional query answering | privacy-accuracy trade-off μ² · err: μ² · err ≥ n; equality for standard Gaussian noise | Helped |
| Use log-concave norm-power noise with density proportional to e^{-||x||_p^α} | Noise-addition mechanisms with sensitivity-1 queries · High-dimensional query answering | ROC function / GDP privacy | No clear effect |
| Increase the noise scale for log-concave noise | Noise-addition mechanisms · Any neighboring-query pair | privacy | Helped |
Closest papers by meaning
- Optimality of Matrix Mechanism on $\ell_p^p$-metric (ICLR 2025 · similarity 0.89)
- Perturb-and-Project: Differentially Private Similarities and Marginals (ICML 2024 · similarity 0.88)
- Efficient and Private Marginal Reconstruction with Local Non-Negativity (NeurIPS 2024 · similarity 0.87)
- Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph (NeurIPS 2025 · similarity 0.87)
- On Differential Privacy for Adaptively Solving Search Problems via Sketching (ICML 2025 · similarity 0.87)
- Privacy amplification by random allocation (NeurIPS 2025 · 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.