PreFair: Privately Generating Justifiably Fair Synthetic Data
Summary: PreFair integrates causal "justifiable fairness" into DP synthetic-data generation, adapting the notion for the synthetic-data setting to enforce fairness. It proves intractability, gives algorithms optimal under assumptions, and empirically yields significantly fairer synthetic data with comparable fidelity to leading DP generators. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. David Pujol (Duke University)
- 2. Amir Gilad (Duke University)
- 3. Ashwin Machanavajjhala (Duke University)
BibTeX Citation
@article{pujol_vldb23,
title = {{PreFair: Privately Generating Justifiably Fair Synthetic Data}},
author = {Pujol, David and Gilad, Amir and Machanavajjhala, Ashwin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {6},
pages = {1573--1586},
doi = {10.14778/3583140.3583168},
url = {https://doi.org/10.14778/3583140.3583168},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,239 | Fair Data Pre-Processing with Imperfect Attribute Space | 2026 | SIGMOD | 5.093636e-05 |
| 10,511 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 5.093636e-05 |
| 10,967 | Privacy-Enhanced Database Synthesis for Benchmark Publishing | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 123 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00031082693 |
| 863 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD | 0.00013531835 |
| 1,144 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB | 0.00011999046 |
| 1,169 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD | 0.00011838753 |
| 1,308 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011216361 |
| 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 9.047803e-05 |
| 3,412 | Kamino: Constraint-Aware Differentially Private Data Synthesis | 2021 | VLDB | 7.4344343e-05 |
| 3,939 | DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing | 2014 | VLDB | 7.008159e-05 |
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| 5 | 10,345 | Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis] | 2026 | SIGMOD |
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| 7 | 11,349 | DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms | 2024 | VLDB |
| 8 | 11,026 | PrivEval: a tool for interactive evaluation of privacy metrics in synthetic data generation | 2025 | VLDB |
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| 10 | 3,016 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB |