mlsanityData to 7 Oct 2026

On Protecting the Data Privacy of Large Language Models (LLMs): A Survey

arXiv 2403.05156 ↗First posted 8 Mar 2024cs.CRcited by 91

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Abstract (arXiv)

Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language. They learn language patterns by analyzing large amounts of text data, allowing them to perform writing, conversation, summarizing and other language tasks. When LLMs process and generate large amounts of data, there is a risk of leaking sensitive information, which may threaten data privacy. This paper concentrates on elucidating the data privacy concerns associated with LLMs to foster a comprehensive understanding. Specifically, a thorough investigation is undertaken to delineate the spectrum of data privacy threats, encompassing both passive privacy leakage and active privacy attacks within LLMs. Subsequently, we conduct an assessment of the privacy protection mechanisms employed by LLMs at various stages, followed by a detailed examination of their efficacy and constraints. Finally, the discourse extends to delineate the challenges encountered and outline prospective directions for advancement in the realm of LLM privacy protection.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 25; 2025: 55; 2026: 11.

91 papers.

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  1. 2025Small Language Models are the Future of Agentic AIcited by 254
  2. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  3. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  4. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  5. 2024Catastrophic Failure of LLM Unlearning via Quantizationcited by 76
  6. 2024A Survey on Data Synthesis and Augmentation for Large Language Modelscited by 40
  7. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  8. 2024A Survey of Useful LLM Evaluationcited by 28
  9. 2025A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcarecited by 27
  10. 2024The Future of AI-Driven Software Engineeringcited by 23
  11. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  12. 2024Multi-Head RAG: Solving Multi-Aspect Problems with LLMscited by 18
38 more
  1. 2025A Survey on Model Extraction Attacks and Defenses for Large Language Modelscited by 18
  2. 2024HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protectioncited by 16
  3. 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
  4. 2025A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakagecited by 15
  5. 2025We Urgently Need Privilege Management in MCP: A Measurement of API Usage in MCP Ecosystemscited by 15
  6. 2024Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directionscited by 14
  7. 2025Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Modelscited by 14
  8. 2024Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approachescited by 13
  9. 2025Towards User-level Private Reinforcement Learning with Human Feedbackcited by 13
  10. 2024Efficient Prompting for LLM-based Generative Internet of Thingscited by 12
  11. 2025LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Datacited by 10
  12. 2025SafeLawBench: Towards Safe Alignment of Large Language Modelscited by 9
  13. 2025From Large to Small: Transferring CUDA Optimization Expertise via Reasoning Graphcited by 9
  14. 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
  15. 2024Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Surveycited by 8
  16. 2024LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM statescited by 8
  17. 2025On the Impact of Noise in Differentially Private Text Rewritingcited by 8
  18. 2025LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasurescited by 8
  19. 2025LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysiscited by 7
  20. 2025Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Callingcited by 7
  21. 2025LLM Meeting Decision Trees on Tabular Datacited by 7
  22. 2025Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Expertscited by 7
  23. 2024Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systemscited by 6
  24. 2024PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMscited by 6
  25. 2024Power-Softmax: Towards Secure LLM Inference over Encrypted Datacited by 6
  26. 2025AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunkingcited by 6
  27. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  28. 2025LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliancecited by 6
  29. 2026G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMscited by 6
  30. 2024Recent Advances in Attack and Defense Approaches of Large Language Modelscited by 5
  31. 2024Exactly Minimax-Optimal Locally Differentially Private Samplingcited by 5
  32. 2024Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directionscited by 5
  33. 2025Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigationcited by 5
  34. 2025A Novel Hat-Shaped Device-Cloud Collaborative Inference Framework for Large Language Modelscited by 5
  35. 2025Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewritingcited by 5
  36. 2025Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysiscited by 5
  37. 2025Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implicationscited by 5
  38. 2025Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistantscited by 4

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