Understanding PII Leakage in Large Language Models: A Systematic Survey
Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fan Zhang
Abstract
Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved challenges in the current research landscape and suggest future research directions.
BibTeX
@inproceedings{ijcai2025_understandingpii,
title = {Understanding PII Leakage in Large Language Models: A Systematic Survey},
author = {Shuai Cheng and Zhao Li and Shu Meng and Mengxia Ren and Haitao Xu and Shuai Hao and Chuan Yue and Fan Zhang},
booktitle = {IJCAI 2025},
year = {2025}
}