mlsanityData to 7 Oct 2026

FFPDG: Fast, Fair and Private Data Generation

arXiv 2307.00161 ↗First posted 30 Jun 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [citegoodfellow2014generative] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 28 of its 63 references that are in the map. First posted on arXiv 30 Jun 2023.

Builds on

28 of 63 references.

References · 28

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  7. 2014Certifying and removing disparate impactcited by 715
  8. 2019Modeling Tabular data using Conditional GANcited by 695
  9. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  10. 2020Label-Only Membership Inference Attackscited by 371
  11. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  12. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
16 more
  1. 2018Scalable Private Learning with PATEcited by 266
  2. 2018Differentially Private Generative Adversarial Networkcited by 235
  3. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  4. 2018Synthesizing Tabular Data using Generative Adversarial Networkscited by 92
  5. 2018Differentially Private Fair Learningcited by 76
  6. 2019Fair Decision Making using Privacy-Protected Datacited by 58
  7. 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
  8. 2020Verifying Individual Fairness in Machine Learning Modelscited by 49
  9. 2019Average Individual Fairness: Algorithms, Generalization and Experimentscited by 46
  10. 2020Fair Bayesian Optimizationcited by 45
  11. 2018Fairness Under Compositioncited by 43
  12. 2021Copula Flows for Synthetic Data Generationcited by 35
  13. 2020ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applicationscited by 29
  14. 2019ProPublica's COMPAS Data Revisitedcited by 24
  15. 2017Optimized Data Pre-Processing for Discrimination Preventioncited by 12
  16. 2021Continual Learning from Synthetic Data for a Humanoid Exercise Robotcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 4; 2025: 1.

8 papers.

  1. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  2. 2024HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agentcited by 12
  3. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  4. 2024Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Studycited by 5
  5. 2023Demographic Parity: Mitigating Biases in Real-World Datacited by 3
  6. 2024PFGuard: A Generative Framework with Privacy and Fairness Safeguardscited by 3
  7. 2025Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspectivecited by 3
  8. 2023FAIRLABEL: Correcting Bias in Labelscited by 0