Research map / Federated learning and privacy / Differential privacy
Incorporating Item Frequency for Differentially Private Set Union
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Policy Laplace with greedy updates without sampling | DPSU · Reddit | set union size: 15485 ± 63 -> 17051 ± 51 | Helped |
| Replace Policy Gaussian with greedy updates without sampling | DPSU · Reddit | set union size: 16958 ± 37 -> 17051 ± 51 | No clear effect |
| Replace Policy Gaussian with greedy updates without sampling | DPSU · Twitter | set union size: 34332 ± 49 -> 37697 ± 22 | Helped |
| Replace Policy Laplace with greedy updates without sampling | DPSU · Finance | set union size: 45323 ± 75 -> 49868 ± 23 | Helped |
| Add public item-frequency knowledge transfer using wiki frequencies | GW · Reddit | set union size: 17051 ± 51 -> 19057 ± 14 | Helped |
| Add public item-frequency knowledge transfer using enron frequencies | GW · Twitter | set union size: 37697 ± 22 -> 42808 ± 26 | Helped |
| Add public item-frequency knowledge transfer using enron frequencies | GW · Finance | set union size: 49868 ± 23 -> 51070 ± 99 | Helped |
| Use public item frequencies for greedy item ordering | GW · DPSU across tested datasets | set union size | Helped |
Closest papers by meaning
- Private Set Union with Multiple Contributions (NeurIPS 2025 · similarity 0.90)
- Differentially Private Set Union (ICML 2020 · similarity 0.89)
- Scalable Private Partition Selection via Adaptive Weighting (ICML 2025 · similarity 0.87)
- Instance-Specific Asymmetric Sensitivity in Differential Privacy (NeurIPS 2024 · similarity 0.86)
- A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy (AAAI 2024 · similarity 0.85)
- A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility (AAAI 2024 · similarity 0.85)
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