# PreFair: Privately Generating Justifiably Fair Synthetic Data

arXiv: https://arxiv.org/abs/2212.10310 · HTML: https://mlsanity.com/paper/2212.10310/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2022.

## Built on by (most cited first, 13 of 13)

- [CuTS: Customizable Tabular Synthetic Data Generation](https://mlsanity.com/paper/2307.03577/) (2023)
- [InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy](https://mlsanity.com/paper/2507.02974/) (2025)
- [Demographic Parity: Mitigating Biases in Real-World Data](https://mlsanity.com/paper/2309.17347/) (2023)
- [Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations](https://mlsanity.com/paper/2407.02073/) (2024)
- [PFGuard: A Generative Framework with Privacy and Fairness Safeguards](https://mlsanity.com/paper/2410.02246/) (2024)
- [CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness](https://mlsanity.com/paper/2509.15199/) (2025)
- [TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees](https://mlsanity.com/paper/2509.19927/) (2025)
- [Unbiased Binning for Fairness-aware Attribute Representation](https://mlsanity.com/paper/2509.21785/) (2025)
- [SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI](https://mlsanity.com/paper/2411.09178/) (2024)
- [FairFinGAN: Fairness-aware Synthetic Financial Data Generation](https://mlsanity.com/paper/2603.05327/) (2026)
- [Fair Data Pre-Processing with Imperfect Attribute Space](https://mlsanity.com/paper/2603.26456/) (2026)
- [Disparate Impact in Synthetic Data Generation](https://mlsanity.com/paper/2606.13105/) (2026)
- [Portable Causal Fairness Across Synthetic Data Generator Families](https://mlsanity.com/paper/2609.03180/) (2026)

## Abstract (arXiv)

When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, endering the synthetic data unfit for use. In this work, we present PreFair, a system that allows for DP fair synthetic data generation. PreFair extends the state-of-the-art DP data generation mechanisms by incorporating a causal fairness criterion that ensures fair synthetic data. We adapt the notion of justifiable fairness to fit the synthetic data generation scenario. We further study the problem of generating DP fair synthetic data, showing its intractability and designing algorithms that are optimal under certain assumptions. We also provide an extensive experimental evaluation, showing that PreFair generates synthetic data that is significantly fairer than the data generated by leading DP data generation mechanisms, while remaining faithful to the private data.