mlsanityData to 7 Oct 2026

Privacy Preserving Machine Learning: Threats and Solutions

arXiv 1804.11238 ↗First posted 27 Mar 2018cs.CR cs.LGcited by 78

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Abstract (arXiv)

For privacy concerns to be addressed adequately in current machine learning systems, the knowledge gap between the machine learning and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 17; 2022: 15; 2023: 21; 2024: 10; 2025: 8; 2026: 6.

78 papers.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2021Trustworthy AI: A Computational Perspectivecited by 93
  3. 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
  4. 2023Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutionscited by 59
  5. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
  6. 2021Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communitiescited by 40
  7. 2023SoK: Privacy-Preserving Data Synthesiscited by 37
  8. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  9. 2024A Review on Machine Unlearningcited by 30
  10. 2023Green Edge AI: A Contemporary Surveycited by 24
  11. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  12. 2025A Survey of Zero-Knowledge Proof Based Verifiable Machine Learningcited by 21
38 more
  1. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  2. 2021Challenges for machine learning in clinical translation of big data imaging studiescited by 15
  3. 2023DualHSIC: HSIC-Bottleneck and Alignment for Continual Learningcited by 15
  4. 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
  5. 2022Differential Privacy in Natural Language Processing: The Story So Farcited by 12
  6. 2023FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learningcited by 12
  7. 2023Learning from Aggregated Data: Curated Bags versus Random Bagscited by 11
  8. 2022On the utility and protection of optimization with differential privacy and classic regularization techniquescited by 10
  9. 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
  10. 2022Privacy Meets Explainability: A Comprehensive Impact Benchmarkcited by 9
  11. 2025STAR: Stability-Inducing Weight Perturbation for Continual Learningcited by 8
  12. 2021Federated Learning Versus Classical Machine Learning: A Convergence Comparisoncited by 7
  13. 2023Disentangling and Operationalizing AI Fairness at LinkedIncited by 7
  14. 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
  15. 2021Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classificationcited by 6
  16. 2024Building a Domain-specific Guardrail Model in Productioncited by 6
  17. 2021SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregationcited by 5
  18. 2022ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scalecited by 4
  19. 2022Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)cited by 4
  20. 2023CoSDA: Continual Source-Free Domain Adaptationcited by 4
  21. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  22. 2022Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challengescited by 3
  23. 2023Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Surveycited by 3
  24. 2021Plinius: Secure and Persistent Machine Learning Model Trainingcited by 2
  25. 2021A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICScited by 2
  26. 2022Towards a Responsible AI Development Lifecycle: Lessons From Information Securitycited by 2
  27. 2022Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbationcited by 2
  28. 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
  29. 2022Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddingscited by 2
  30. 2023PrIeD-KIE: Towards Privacy Preserved Document Key Information Extractioncited by 2
  31. 2023Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examplescited by 2
  32. 2024Learning from Aggregate responses: Instance Level versus Bag Level Loss Functionscited by 2
  33. 2024The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacycited by 2
  34. 2025Towards deployment-centric multimodal AI beyond vision and languagecited by 2
  35. 2025Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Userscited by 2
  36. 2021Efficient CNN Building Blocks for Encrypted Datacited by 1
  37. 2021Efficient Encrypted Inference on Ensembles of Decision Treescited by 1
  38. 2021Supervised Machine Learning with Plausible Deniabilitycited by 1

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