Dataset Ownership in the Era of Large Language Models

2025-09-07

AI Summary

In the era of large language models, dataset copyright protection faces significant challenges, including legal frameworks lagging behind technological development and opaque technical environments. This paper systematically proposes three technical approaches to dataset copyright protection: non-intrusive (e.g., digital watermarking), semi-intrusive (e.g., reversible data perturbation), and highly intrusive (e.g., reversible adversarial examples). It introduces the first comprehensive classification framework covering ownership authentication, usage monitoring, and misuse traceability. By integrating watermark embedding, adversarial sample generation, and model provenance analysis, the approach enables autonomous, law-procedure-free dataset copyright verification. The study rigorously characterizes the applicability boundaries and inherent limitations of each method. Key future research directions are identified as scalability, cross-platform standardization, and ethical compliance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

As datasets become critical assets in modern machine learning systems, ensuring robust copyright protection has emerged as an urgent challenge. Traditional legal mechanisms often fail to address the technical complexities of digital data replication and unauthorized use, particularly in opaque or decentralized environments. This survey provides a comprehensive review of technical approaches for dataset copyright protection, systematically categorizing them into three main classes: non-intrusive methods, which detect unauthorized use without modifying data; minimally-intrusive methods, which embed lightweight, reversible changes to enable ownership verification; and maximally-intrusive methods, which apply aggressive data alterations, such as reversible adversarial examples, to enforce usage restrictions. We synthesize key techniques, analyze their strengths and limitations, and highlight open research challenges. This work offers an organized perspective on the current landscape and suggests future directions for developing unified, scalable, and ethically sound solutions to protect datasets in increasingly complex machine learning ecosystems.

Problem

• Ensuring robust copyright protection for datasets
• Addressing unauthorized use in opaque or decentralized environments
• Developing technical approaches for dataset ownership verification

Innovation

• Non-intrusive unauthorized use detection
• Minimally-intrusive reversible ownership verification
• Maximally-intrusive adversarial usage restrictions