A Survey of Zero-Shot Sensitive Information Detection Techniques based on Large Language Models

Abstract

With the rapid growth of digital information, the risk of sensitive information leakage in textual data, including personally identifiable information, medical privacy, financial data, and corporate confidential information, has become increasingly prominent. Traditional sensitive information detection methods, which mainly rely on rule matching, supervised learning, and manual annotation, struggle to meet the requirements of identifying diverse, open-domain, and dynamically evolving sensitive i

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@article{wei2026survey,
  title = {{A Survey of Zero-Shot Sensitive Information Detection Techniques based on Large Language Models}},
  author = {Jie-Qun Wei and Yuejin Zhang},
  year = {2026},
  month = aug,
  journal = {Scientific Journal of Intelligent Systems Research},
  doi = {10.54691/3kzsns60},
  url = {https://www.semanticscholar.org/paper/a99698990062ff0248f8281a1c8f3f064b1c96bf}
}