Research map / Federated learning and privacy / Differential privacy
Subspace Differential Privacy
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use projected Laplace noise while enforcing state population totals | County-level 2020 Census population counts · 2020 Census demonstration data | error bias | Helped |
| Enforce linear equality invariants by projecting Gaussian noise into the invariant null space | Hourly group-by-building counts · Tsinghua campus mobility data | error scale | Mixed |
| Use extension Gaussian noise calibrated to the null-space query sensitivity instead of projected Gaussian noise calibrated to the full query | Induced subspace Gaussian mechanism · General queries | mean squared error | Helped |
| Use projected Laplace noise with the state population invariant | County-level Census counts · 2020 Census demonstration data | negative error association with true county population: 37 out of 48; 11 statistically significant at α = 0.01 | Helped |
Closest papers by meaning
- Integer Subspace Differential Privacy (AAAI 2023 · similarity 0.94)
- Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases (NeurIPS 2019 · similarity 0.88)
- Distributed Differentially Private Data Analytics via Secure Sketching (ICML 2025 · similarity 0.88)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.87)
- Universal Exact Compression of Differentially Private Mechanisms (NeurIPS 2024 · similarity 0.87)
- On Differential Privacy for Adaptively Solving Search Problems via Sketching (ICML 2025 · similarity 0.87)
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