mlsanityData to 7 Oct 2026

Decision Making with Differential Privacy under a Fairness Lens

arXiv 2105.07513 ↗First posted 16 May 2021cs.AI cs.CY cs.LGcited by 32

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Abstract (arXiv)

Agencies, such as the U.S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform to privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private data sets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. {The paper shows that, when the decisions take as input differentially private data}, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 16 May 2021.

Builds on

12 of 29 references.

References · 12

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2013The Composition Theorem for Differential Privacycited by 290
  3. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  4. 2020Fairness in Machine Learningcited by 194
  5. 2018Differentially Private Fair Learningcited by 76
  6. 2019Fair Decision Making using Privacy-Protected Datacited by 58
  7. 2020Differentially Private and Fair Deep Learning: A Lagrangian Dual Approachcited by 53
  8. 2020LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scalecited by 26
  9. 2017Towards Practical Differential Privacy for SQL Queriescited by 19
  10. 2020Bias and Variance of Post-processing in Differential Privacycited by 14
  11. 2020Differential Privacy of Hierarchical Census Data: An Optimization Approachcited by 9
  12. 2018An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choicescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 7; 2023: 8; 2024: 4; 2025: 4; 2026: 3.

32 papers.

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  1. 2021Iterative Methods for Private Synthetic Data: Unifying Framework and New Methodscited by 58
  2. 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
  3. 2021Node-Level Differentially Private Graph Neural Networkscited by 43
  4. 2022Pruning has a disparate impact on model accuracycited by 43
  5. 2021DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?cited by 32
  6. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  7. 2023Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingcited by 17
  8. 2022Stochastic Differentially Private and Fair Learningcited by 16
  9. 2021A Fairness Analysis on Private Aggregation of Teacher Ensemblescited by 11
  10. 2022Post-processing of Differentially Private Data: A Fairness Perspectivecited by 9
  11. 2022SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensemblescited by 8
  12. 2022Doubly Fair Dynamic Pricingcited by 8
20 more
  1. 2025Differential privacy for medical deep learning: methods, tradeoffs, and deployment implicationscited by 7
  2. 2021DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?cited by 6
  3. 2023Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacycited by 6
  4. 2023On the Fairness Impacts of Private Ensembles Modelscited by 4
  5. 2024Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient datacited by 4
  6. 2025Trustworthy AI Must Account for Interactionscited by 4
  7. 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacycited by 4
  8. 2022Exploring the Unfairness of DP-SGD Across Settingscited by 3
  9. 2023FairDP: Certified Fairness with Differential Privacycited by 3
  10. 2023Privacy and Bias Analysis of Disclosure Avoidance Systemscited by 2
  11. 2023On The Fairness Impacts of Hardware Selection in Machine Learningcited by 2
  12. 2024Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processescited by 2
  13. 2024Privacy at a Price: Exploring its Dual Impact on AI Fairnesscited by 1
  14. 2021Partial sensitivity analysis in differential privacycited by 0
  15. 2023Price-Aware Deep Learning for Electricity Marketscited by 0
  16. 2024Differentially Private Data Release on Graphs: Inefficiencies and Unfairnesscited by 0
  17. 2025Optimal Allocation of Privacy Budget on Hierarchical Data Releasecited by 0
  18. 2026Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimationcited by 0
  19. 2026Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithmscited by 0
  20. 2026The Privacy-Hallucination Tradeoff in Differentially Private Language Modelscited by 0