Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
Summary: Introduce 'epistemic parity': measure how often peer-reviewed empirical conclusions (reproduced on ICPSR datasets) persist when rerun on DP synthetic data. Benchmark shows SOTA synthesizers often achieve high parity at practical ε but some claims remain unreproducible, motivating utility-first DP mechanisms and application-specific risk models. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Lucas Rosenblatt
- 2. Bernease Herman
- 3. Anastasia Holovenko
- 4. Wonkwon Lee
- 5. Joshua Loftus
- 6. Elizabeth McKinnie
- 7. Taras Rumezhak
- 8. Andrii Stadnik
- 9. Bill Howe
- 10. Julia Stoyanovich
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 136 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.0004241101 |
| 1,446 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD | 0.0001194108 |
| 2,434 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 8.8278955e-05 |
| 2,465 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7518123e-05 |
| 2,881 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 7.9665978e-05 |
| 3,329 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 7.2156424e-05 |
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