Protecting Personal Identifiable Information (PII) in text data is crucial for privacy, but current PII generalization methods face challenges such as uneven data distributions and limited context awareness. To address these issues, we propose two approaches: a feature-based method using machine learning to improve performance on structured inputs, and a novel context-aware framework that considers the broader context and semantic relationships between the original text and generalized candidates. The context-aware approach employs Multilingual-BERT for text representation, functional transformations, and mean squared error scoring to evaluate candidates. Experiments on the WikiReplace dataset demonstrate the effectiveness of both methods, with the context-aware approach outperforming the feature-based one across different scales. This work contributes to advancing PII generalization techniques by highlighting the importance of feature selection, ensemble learning, and incorporating contextual information for better privacy protection in text anonymization.
PII-Bench: Evaluating Query-Aware Privacy Protection Systems
Institute of Fintech, Fudan University（复旦大学金融科技学院）；Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University（复旦大学数据科学上海重点实验室）；Laboratory of Data Analytics and Security, Fudan University（复旦大学数据安全分析实验室）
ProPILE: Probing Privacy Leakage in Large Language Models
NAVER AI Lab（NAVER人工智能实验室）；University of Luxembourg（卢森堡大学）；Parameter Lab（帕拉米特实验室）；Seoul National University（首尔国立大学）；University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）
AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers
Indian Institute of Technology Guwahati（印度理工学院古瓦哈蒂分校）
