mlsanityData to 14 Oct 2026

Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective

arXiv 2311.18252 ↗First posted 30 Nov 2023cs.SE cs.AI cs.CYcited by 6

On the map: LLM safety and security → Watermarking generated content and models

Abstract (arXiv)

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 11 of its 29 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

11 of 29 references.

References · 11

  1. 2020Extracting Training Data from Large Language Modelscited by 2,189
  2. 2019Machine Unlearningcited by 1,061
  3. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 919
  4. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
  5. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 498
  6. 2019Making AI Forget You: Data Deletion in Machine Learningcited by 465
  7. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  8. 2022Differentially Private Bias-Term Fine-tuning of Foundation Modelscited by 48
  9. 2021CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoningcited by 47
  10. 2021Copyright in Generative Deep Learningcited by 29
  11. 2023On the Way to SBOMs: Investigating Design Issues and Solutions in Practicecited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2025RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelinescited by 5
  2. 2024Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessmentcited by 2
  3. 2025ACAI for SBOs: AI Co-creation for Advertising and Inspiration for Small Business Ownerscited by 2
  4. 2024Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implicationscited by 1
  5. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
  6. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Rethinking Data Protection in the (Generative) Artificial Intelligence Era
  2. 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Review
  3. 2025SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
  4. 2025How Do Data Owners Say No? A Case Study of Data Consent Mechanisms in Web-Scraped Vision-Language AI Training Datasets
  5. 2026Privilege and confidentiality in generative AI workflows
  6. 2026Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era
  7. 2024Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
  8. 2025When Privacy Isn't Synthetic: Hidden Data Leakage in Generative AI Models