现代情报2026，Vol.46Issue(4)：136-148,13.DOI:10.3969/j.issn.1008-0821.2026.04.012
融合敏感性与关联性的隐私风险评估及实证研究
Privacy Risk Assessment for User Generated Content:A Framework Incorporating Sensitivity and Correlation
摘要
Abstract
[Purpose/Significance]This study aims to evaluate the privacy risks associated with unstructured text within online community user-generated content,addressing the challenge that such data poses as a high-risk vector for privacy leakage due to contextual variations in sensitivity and correlation-based vulnerabilities.[Method/Process]A pri-vacy risk quantification framework integrating sensitivity and relevance was proposed,with experimental validation con-ducted on the'Member Networking'section of the academic platform Muchong.This framework employs a BERT-BiLSTM-CRF deep learning model to achieve attribute extraction from unstructured text.Attribute sensitivity was quanti-fied using a privacy lexicon,attribute correlation was measured via Pointwise Mutual Information(PMI),and these factors were integrated with privacy principal identification metrics to compute privacy risk scores,followed by risk stratification.[Result/Conclusion]Ablation studies and manual validation demonstrate its capability to identify,assess,and stratify pri-vacy risks in unstructured textual data.These findings offer new insights for improving privacy protection policies and plat-form privacy governance.关键词
敏感性/关联性/隐私风险评估/在线社区/小木虫/BERT-BiLSTM-CRFKey words
sensitivity/correlation/privacy risk assessment/online community/Muchong/BERT-BiLSTM-CRF分类
社会科学引用本文复制引用
耿瑞利,张天天,芦哲,李森涛,鲁晓明,王锦科..融合敏感性与关联性的隐私风险评估及实证研究[J].现代情报,2026,46(4):136-148,13.基金项目
国家社会科学基金一般项目"突发公共事件衍生数据隐私风险的识别与消减机制研究"(项目编号:22BTQ074). （项目编号:22BTQ074）
