mlsanityData to 7 Oct 2026

Fair Learning with Private Demographic Data

arXiv 2002.11651 ↗First posted 26 Feb 2020cs.LG cs.CY stat.MLcited by 46

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Abstract (arXiv)

Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that allows individuals to release their sensitive information privately while still allowing any downstream entity to learn non-discriminatory predictors. We show how to adapt non-discriminatory learners to work with privatized protected attributes giving theoretical guarantees on performance. Finally, we highlight how the methodology could apply to learning fair predictors in settings where protected attributes are only available for a subset of the data.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 8; 2023: 15; 2024: 9; 2025: 5; 2026: 1.

46 papers.

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  1. 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
  2. 2022Disparate Impact in Differential Privacy from Gradient Misalignmentcited by 31
  3. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  4. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  5. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  6. 2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
  7. 2022Differential Privacy has Bounded Impact on Fairness in Classificationcited by 18
  8. 2022Fair Ranking with Noisy Protected Attributescited by 17
  9. 2021Fair Sequential Selection Using Supervised Learning Modelscited by 16
  10. 2022Stochastic Differentially Private and Fair Learningcited by 16
  11. 2023Unraveling Privacy Risks of Individual Fairness in Graph Neural Networkscited by 16
  12. 2023Unlocking Accuracy and Fairness in Differentially Private Image Classificationcited by 16
34 more
  1. 2021A Fairness Analysis on Private Aggregation of Teacher Ensemblescited by 11
  2. 2023Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utilitycited by 10
  3. 2021Federated Learning Meets Fairness and Differential Privacycited by 9
  4. 2022SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensemblescited by 8
  5. 2022When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributescited by 6
  6. 2023When Fair Classification Meets Noisy Protected Attributescited by 6
  7. 2024A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Resultscited by 6
  8. 2023(Local) Differential Privacy has NO Disparate Impact on Fairnesscited by 5
  9. 2024Differentially Private Post-Processing for Fair Regressioncited by 5
  10. 2021Fairness-Driven Private Collaborative Machine Learningcited by 4
  11. 2023On the Fairness Impacts of Private Ensembles Modelscited by 4
  12. 2023Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learningcited by 4
  13. 2023On the Impact of Multi-dimensional Local Differential Privacy on Fairnesscited by 4
  14. 2023SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learningcited by 4
  15. 2023Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Accesscited by 3
  16. 2023FairDP: Certified Fairness with Differential Privacycited by 3
  17. 2024Regulation Games for Trustworthy Machine Learningcited by 3
  18. 2024Differentially Private Fair Binary Classificationscited by 3
  19. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  20. 2024PFGuard: A Generative Framework with Privacy and Fairness Safeguardscited by 3
  21. 2022Outlier-Robust Group Inference via Gradient Space Clusteringcited by 2
  22. 2023Group Fairness with Uncertainty in Sensitive Attributescited by 2
  23. 2023Sampling Individually-Fair Rankings that are Always Group Faircited by 2
  24. 2024Combining Machine Learning Defenses without Conflictscited by 2
  25. 2025Discrimination-free Insurance Pricing with Privatized Sensitive Attributescited by 1
  26. 2021Statistical Guarantees for Fairness Aware Plug-In Algorithmscited by 0
  27. 2023Survey of Trustworthy AI: A Meta Decision of AIcited by 0
  28. 2024Fairness Risks for Group-conditionally Missing Demographicscited by 0
  29. 2024Differentially Private Data Release on Graphs: Inefficiencies and Unfairnesscited by 0
  30. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  31. 2025Private Rate-Constrained Optimization with Applications to Fair Learningcited by 0
  32. 2025FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utilitycited by 0
  33. 2025Optimal Fairness under Local Differential Privacycited by 0
  34. 2026Fairness risk and its privacy-enabled solution in AI-driven robotic applicationscited by 0