Research map / Federated learning and privacy / Differential privacy
Differentially Private Query Release Through Adaptive Projection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a nonadaptive projection (T=1) instead of iteratively selecting the highest-error remaining queries (ablation) | Adaptive Projection · Adult, workload 64, 3-way marginals | query error | Hurt |
| Vary synthetic dataset size n' (ablation) | Adaptive Projection · Several datasets and workloads | total runtime | Mixed |
| Increase synthetic dataset size n' (ablation) | Adaptive Projection · Several datasets and workloads | runtime per iteration | Hurt |
| Set synthetic dataset size to n'=1000 (ablation) | Adaptive Projection · Several datasets and workloads | error | Helped |
Closest papers by meaning
- Efficient and Private Marginal Reconstruction with Local Non-Negativity (NeurIPS 2024 · similarity 0.90)
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- Private Evolution Converges (NeurIPS 2025 · similarity 0.86)
- Privacy Amplification Persists under Unlimited Synthetic Data Release (NeurIPS 2026 · similarity 0.86)
- Private Prediction via Shrinkage (NeurIPS 2026 · similarity 0.86)
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