Research map / Federated learning and privacy / Differential privacy
Local Private Hypothesis Testing: Chi-Square Tests
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LocalExpGOF instead of LocalBitFlipGOF or LocalNoiseGOF | Locally private goodness-of-fit testing · Uniform null, d = 4, η = 0.01 | power | Helped |
| Use LocalExpGOF instead of LocalBitFlipGOF or LocalNoiseGOF | Locally private goodness-of-fit testing · Uniform null, d = 40, ε = 4, η = 0.005 | power | Helped |
| Use LocalBitFlipGOF instead of LocalExpGOF or LocalNoiseGOF | Locally private goodness-of-fit testing · Uniform null, d = 40, ε = 2, η = 0.005 | power | Helped |
Closest papers by meaning
- Minimax Optimal Two-Sample Testing under Local Differential Privacy (NeurIPS 2026 · similarity 0.88)
- A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility (AAAI 2024 · similarity 0.87)
- Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph (NeurIPS 2025 · similarity 0.87)
- Sort, Partition, Randomize: Optimal Binary Hypothesis Testing under Local Differential Privacy (NeurIPS 2026 · similarity 0.87)
- Distributed Differentially Private Data Analytics via Secure Sketching (ICML 2025 · similarity 0.87)
- Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime (NeurIPS 2024 · similarity 0.86)
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