mlsanityData to 7 Oct 2026

Data Privacy and Trustworthy Machine Learning

arXiv 2209.06529 ↗First posted 14 Sep 2022cs.LG cs.CRcited by 6

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Abstract (arXiv)

The privacy risks of machine learning models is a major concern when training them on sensitive and personal data. We discuss the tradeoffs between data privacy and the remaining goals of trustworthy machine learning (notably, fairness, robustness, and explainability).

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 14 Sep 2022.

Builds on

10 of 19 references.

References · 10

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  5. 2019White-box vs Black-box: Bayes Optimal Strategies for Membership Inferencecited by 265
  6. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 237
  7. 2019Privacy Risks of Securing Machine Learning Models against Adversarial Examplescited by 136
  8. 2020On the Privacy Risks of Algorithmic Fairnesscited by 82
  9. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
  10. 2020ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learningcited by 66

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Cited by year (2026 to 7 Oct)

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

6 papers.

  1. 2024A General Framework for Learning from Weak Supervisioncited by 7
  2. 2023SoK: Unintended Interactions among Machine Learning Defenses and Riskscited by 6
  3. 2025Enabling Group Fairness in Graph Unlearning via Bi-level Debiasingcited by 3
  4. 2025Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Reviewcited by 2
  5. 2023Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Modelscited by 1
  6. 2026SoK: Colluding Adversaries in Machine Learning Pipelinescited by 0