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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)

Paper ID
13019
Venue
VLDB
Year
2023
Pagerank
4.4853979e-05
Overall Rank
8,609 | 40.11%
DOI
10.14778/3583140.3583168

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,223 On Fair Epsilon Net and Geometric Hitting Set 2026 VLDB 4.1945683e-05
10,724 Privacy-Enhanced Database Synthesis for Benchmark Publishing 2025 VLDB 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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