mlsanityData to 7 Oct 2026

Rethinking Data Protection in the (Generative) Artificial Intelligence Era

arXiv 2507.03034 ↗First posted 3 Jul 2025cs.LG cs.AI cs.CRcited by 17

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Abstract (arXiv)

The (generative) artificial intelligence (AI) era has profoundly reshaped the meaning and value of data. No longer confined to static content, data now permeates every stage of the AI lifecycle from the training samples that shape model parameters to the prompts and outputs that drive real-world model deployment. This shift renders traditional notions of data protection insufficient, while the boundaries of what needs safeguarding remain poorly defined. Failing to safeguard data in AI systems can inflict societal and individual, underscoring the urgent need to clearly delineate the scope of and rigorously enforce data protection. In this perspective, we propose a four-level taxonomy, including non-usability, privacy preservation, traceability, and deletability, that captures the diverse protection needs arising in modern (generative) AI models and systems. Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline, including training datasets, model weights, system prompts, and AI-generated content. We analyze representative technical approaches at each level and reveal regulatory blind spots that leave critical assets exposed. By offering a structured lens to align future AI technologies and governance with trustworthy data practices, we underscore the urgency of rethinking data protection for modern AI techniques and provide timely guidance for developers, researchers, and regulators alike.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 65 of its 138 references that are in the map. First posted on arXiv 3 Jul 2025.

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65 of 138 references.

References · 65

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  6. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  8. 2016Deep Learning with Differential Privacycited by 2,682
  9. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  10. 2020Extracting Training Data from Large Language Modelscited by 2,144
  11. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  12. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
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  1. 2020Knowledge Distillation: A Surveycited by 1,578
  2. 2019Machine Unlearningcited by 1,020
  3. 2022Diffusion Models in Vision: A Surveycited by 1,008
  4. 2019Certified Data Removal from Machine Learning Modelscited by 512
  5. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  6. 2020Backdoor Learning: A Surveycited by 412
  7. 2023Towards Generalist Biomedical AIcited by 286
  8. 2018Distributed learning of deep neural network over multiple agentscited by 277
  9. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Modelscited by 200
  10. 2021Unlearnable Examples: Making Personal Data Unexploitablecited by 188
  11. 2023RADAR: Robust AI-Text Detection via Adversarial Learningcited by 187
  12. 2020A Survey of Privacy Attacks in Machine Learningcited by 143
  13. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 128
  14. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
  15. 2023Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineeringcited by 86
  16. 2022Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protectioncited by 80
  17. 2022I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defencescited by 75
  18. 2019IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundarycited by 68
  19. 2024SafeGen: Mitigating Sexually Explicit Content Generation in Text-to-Image Modelscited by 64
  20. 2023Locally Differentially Private Document Generation Using Zero Shot Promptingcited by 63
  21. 2022Black-box Dataset Ownership Verification via Backdoor Watermarkingcited by 57
  22. 2020Adv-watermark: A Novel Watermark Perturbation for Adversarial Examplescited by 56
  23. 2023Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Handcited by 52
  24. 2021Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorizationcited by 50
  25. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  26. 2023Prompt Stealing Attacks Against Text-to-Image Generation Modelscited by 48
  27. 2021Defending against Model Stealing via Verifying Embedded External Featurescited by 44
  28. 2024SoK: Watermarking for AI-Generated Contentcited by 43
  29. 2024RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detectioncited by 36
  30. 2024Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attributioncited by 35
  31. 2025Towards Label-Only Membership Inference Attack against Pre-trained Large Language Modelscited by 34
  32. 2023PromptCARE: Prompt Copyright Protection by Watermark Injection and Verificationcited by 30
  33. 2023Certified Minimax Unlearning with Generalization Rates and Deletion Capacitycited by 29
  34. 2024SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Modelscited by 26
  35. 2022FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Modelcited by 25
  36. 2024WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Modelscited by 25
  37. 2022MOVE: Effective and Harmless Ownership Verification via Embedded External Featurescited by 20
  38. 2024SafeEar: Content Privacy-Preserving Audio Deepfake Detectioncited by 20

50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 11; 2026: 6.

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  1. 2025Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inferencecited by 19
  2. 2025SoK: Large Language Model Copyright Auditing via Fingerprintingcited by 14
  3. 2025AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editingcited by 9
  4. 2025DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspectivecited by 8
  5. 2025Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuningcited by 8
  6. 2025NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detectioncited by 7
  7. 2025Beyond Data Privacy: New Privacy Risks for Large Language Modelscited by 6
  8. 2025DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modelingcited by 5
  9. 2025SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarkingcited by 3
  10. 2026Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothingcited by 3
  11. 2025Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Modelscited by 2
  12. 2025External Data Extraction Attacks against Retrieval-Augmented Large Language Modelscited by 2
5 more
  1. 2026Intellectual Property Protection for 3D Gaussian Splatting Assets: A Surveycited by 2
  2. 2026JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimizationcited by 2
  3. 2026Echoes within the Reasoning: Stealthy and Effective Watermarking via Chain of Thoughtcited by 2
  4. 2026LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Modelscited by 1
  5. 2026Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customizationcited by 1

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  6. 2025How Do Data Owners Say No? A Case Study of Data Consent Mechanisms in Web-Scraped Vision-Language AI Training Datasets
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