Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing
Summary: Introduces reconstruction advantage, a unified disclosure-risk metric for DP that subsumes membership, attribute inference, and reconstruction. Derives tight noise-to-risk bounds and optimal attacks, enabling principled noise calibration and systematic DP auditing beyond ReRo. (summarized by gpt-5.4-mini on May 27 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 83 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00053910987 |
| 5,512 | Real-World Trajectory Sharing with Local Differential Privacy | 2021 | VLDB | 5.4699659e-05 |
| 7,799 | Quantifying identifiability to choose and audit epsilon in differentially private deep learning | 2021 | VLDB | 4.6438053e-05 |
| 8,275 | Synthetic Tabular Data: Methods, Attacks and Defenses | 2025 | VLDB | 4.5392079e-05 |
| 8,277 | Measuring Re-identification Risk | 2023 | SIGMOD | 4.5392079e-05 |
| 8,306 | Privacy Preserving Serial Data Publishing By Role Composition | 2008 | VLDB | 4.5392079e-05 |
| 11,229 | On the Risks of Collecting Multidimensional Data Under Local Differential Privacy | 2023 | VLDB | 4.1905499e-05 |
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