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Dataset Ownership in the Era of Large Language Models

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Kun Li
Cheng Wang
Minghui Xu
Yue Zhang
Xiuzhen Cheng
Published in
2025 International Conference on Blockchain and Web3.0 Technology Innovation and Application Exchange
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the critical challenges of dataset ownership protection in the era of large language models, focusing on the inadequacies of traditional intellectual property methods. It categorizes protection techniques into three distinct approaches: non-intrusive, minimally-intrusive, and maximally-intrusive, each with its own trade-offs between ownership verification accuracy and data utility. The chapter highlights the over-reliance on image datasets in current research and the urgent need for efficient, scalable solutions that avoid performance degradation in foundation models. It also explores the ethical and legal considerations for ownership transparency, emphasizing the importance of responsible dataset protection in evolving ML ecosystems. By providing a comprehensive review of technical methods and identifying critical gaps, this chapter offers valuable insights for professionals seeking to safeguard dataset ownership in the era of large language models.
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previous chapter Value Representation of Industrial Internet Data Elements Based on Digital Object Architecture
next chapter Incentive Mechanisms for Collaborative Intelligence Sharing in Blockchain-Based Federated LLM Fine-Tuning
Title
Dataset Ownership in the Era of Large Language Models
Authors
Kun Li
Cheng Wang
Minghui Xu
Yue Zhang
Xiuzhen Cheng
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
2025 International Conference on Blockchain and Web3.0 Technology Innovation and Application Exchange

Print ISBN: 978-981-9541-41-6

Electronic ISBN: 978-981-9541-42-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4142-3_27
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