mlsanityData to 7 Oct 2026

Blind Justice: Fairness with Encrypted Sensitive Attributes

arXiv 1806.03281 ↗First posted 8 Jun 2018stat.ML cs.CR cs.CYcited by 41

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Abstract (arXiv)

Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact, sensitive attributes must be examined, e.g., in order to learn a fair model, or to check if a given model is fair. We introduce methods from secure multi-party computation which allow us to avoid both. By encrypting sensitive attributes, we show how an outcome-based fair model may be learned, checked, or have its outputs verified and held to account, without users revealing their sensitive attributes.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 11; 2023: 9; 2024: 7; 2025: 3; 2026: 1.

41 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2022Detecting Shortcut Learning for Fair Medical AI using Shortcut Testingcited by 52
  3. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  4. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  5. 2021Fairness without the sensitive attribute via Causal Variational Autoencodercited by 21
  6. 2022Differential Privacy has Bounded Impact on Fairness in Classificationcited by 18
  7. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  8. 2021Removing biased data to improve fairness and accuracycited by 17
  9. 2021Multiaccurate Proxies for Downstream Fairnesscited by 17
  10. 2022Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributescited by 17
  11. 2022Stochastic Differentially Private and Fair Learningcited by 16
  12. 2022Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairnesscited by 13
29 more
  1. 2024FairProof : Confidential and Certifiable Fairness for Neural Networkscited by 13
  2. 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11
  3. 2022Federated learning: Applications, challenges and future directionscited by 11
  4. 2021Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approachcited by 9
  5. 2022Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivitycited by 8
  6. 2023A Sequentially Fair Mechanism for Multiple Sensitive Attributescited by 8
  7. 2024An Auditing Test To Detect Behavioral Shift in Language Modelscited by 8
  8. 2023Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systemscited by 7
  9. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  10. 2024Secure and Confidential Certificates of Online Fairnesscited by 7
  11. 2024FairJob: A Real-World Dataset for Fairness in Online Systemscited by 6
  12. 2023Attesting Distributional Properties of Training Data for Machine Learningcited by 5
  13. 2021Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Surveycited by 4
  14. 2021Fairness-Driven Private Collaborative Machine Learningcited by 4
  15. 2022PrivFair: a Library for Privacy-Preserving Fairness Auditingcited by 4
  16. 2023Fairness Under Demographic Scarce Regimecited by 4
  17. 2023Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learningcited by 4
  18. 2023SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learningcited by 4
  19. 2022"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learningcited by 3
  20. 2024Referee-Meta-Learning for Fast Adaptation of Locational Fairnesscited by 3
  21. 2023Mitigating Cross-client GANs-based Attack in Federated Learningcited by 2
  22. 2024Formalising Anti-Discrimination Law in Automated Decision Systemscited by 2
  23. 2025P2NIA: Privacy-Preserving Non-Iterative Auditingcited by 2
  24. 2021MPC-Friendly Commitments for Publicly Verifiable Covert Securitycited by 1
  25. 2022Federated Graph-based Networks with Shared Embeddingcited by 1
  26. 2024CCBNet: Confidential Collaborative Bayesian Networks Inferencecited by 1
  27. 2025Proportional Selection in Networkscited by 1
  28. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  29. 2026Certification of Machine Learning Models via Directional Sharpnesscited by 0