LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization
As large language models (LLMs) become widely deployed, their outputs can be copied, transformed, and redistributed at scale without reliable evidence of origin, creating risks for trust, accountability, intellectual property (IP) protection, and high-stakes decision-making. LLM watermarking addresses this problem by embedding detectable signals into text during or after generation. However, existing methods vary in design assumptions, threat models, and evaluation criteria, while deployment choices such as watermark placement, detection authority, and key management affect reliability, security, and scalability. This paper systematizes LLM watermarking as provenance infrastructure for la...
| Authors | Huy Phan, Kieu Dang, Ojaswi Dulal et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 July 2026 |
| Last updated at source | 2 September 2026 |
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