mlsanityData to 7 Oct 2026

Evaluating the Fairness Impact of Differentially Private Synthetic Data

arXiv 2205.04321 ↗First posted 9 May 2022cs.LGcited by 9

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Abstract (arXiv)

Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information. Due to the suppression of underrepresented classes that is often required to achieve privacy, however, it may be in conflict with fairness. We evaluate four DP synthesizers and present empirical results indicating that three of these models frequently degrade fairness outcomes on downstream binary classification tasks. We draw a connection between fairness and the proportion of minority groups present in the generated synthetic data, and find that training synthesizers on data that are pre-processed via a multi-label undersampling method can promote more fair outcomes without degrading accuracy.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 9 May 2022.

Builds on

9 of 17 references.

References · 9

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2019Modeling Tabular data using Conditional GANcited by 695
  5. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  6. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  7. 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 101
  8. 2019Graphical-model based estimation and inference for differential privacycited by 73
  9. 2020Differentially Private Synthetic Data: Applied Evaluations and Enhancementscited by 36

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 0; 2026: 3.

9 papers.

  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  3. 2024Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalizationcited by 5
  4. 2024PFGuard: A Generative Framework with Privacy and Fairness Safeguardscited by 3
  5. 2023A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamicscited by 1
  6. 2026Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Datacited by 1
  7. 2024SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AIcited by 0
  8. 2026Disparate Impact in Synthetic Data Generationcited by 0
  9. 2026Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithmscited by 0