Research map / Federated learning and privacy / Differential privacy
From Robustness to Privacy and Back
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply the inverse-sensitivity-based robust-to-private transformation to a robust estimator (instead of prior PTR/smooth sensitivity methods) | robust-to-private conversion · low-dimensional statistical estimation | estimation error vs. optimal private error | Helped |
| Replace the exact inverse-sensitivity mechanism with its smooth (rho-smoothed) version for continuous outputs | inverse-sensitivity mechanism · continuous estimation tasks | error bound | No clear effect |
Closest papers by meaning
- Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation (NeurIPS 2022 · similarity 0.93)
- Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy (NeurIPS 2026 · similarity 0.91)
- The Limits of Differential Privacy in Online Learning (NeurIPS 2024 · similarity 0.89)
- Certification for Differentially Private Prediction in Gradient-Based Training (ICML 2025 · similarity 0.88)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.88)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.88)
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