社交媒体中自然语言处理隐私风险识别（NLP-PRISM）：一项调查
NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey
AI总结：
本文提出NLP-PRISM框架，系统评估社交媒体中NLP处理的隐私风险，揭示模型隐私保护与效用之间的权衡，并呼吁加强隐私保护措施。
摘要：
自然语言处理（NLP）在社交媒体分析中至关重要，但经常处理包含个人可识别信息（PII）、行为线索和元数据的内容，从而引发隐私风险，如监视、画像和定向广告。为了系统评估这些风险，我们回顾了203篇同行评审论文，并提出了NLP隐私风险识别在社交媒体中的框架（NLP-PRISM），该框架在六个维度上评估漏洞：数据收集、预处理、可见性、公平性、计算风险和合规性。我们的分析显示，转换器模型在F1分数上达到0.58-0.84，但在隐私保护微调下，F1分数会下降1% -23%。使用NLP-PRISM，我们检查了六个NLP任务中的隐私覆盖情况：情感分析（16）、情绪检测（14）、冒犯性语言识别（19）、混合语言处理（39）、原生语言识别（29）和方言检测（24），揭示了隐私研究中的重大差距。我们进一步发现模型效用存在（减少2% -9%）的权衡，MIA AUC（成员推断攻击）为0.81，AIA准确率（属性推断攻击）为0.75。最后，我们呼吁更强的匿名化、隐私意识学习和公平驱动训练，以在社交媒体背景下实现伦理的NLP。
英文摘要：
Natural Language Processing (NLP) is integral to social media analytics but often processes content containing Personally Identifiable Information (PII), behavioral cues, and metadata raising privacy risks such as surveillance, profiling, and targeted advertising. To systematically assess these risks, we review 203 peer-reviewed papers and propose the NLP Privacy Risk Identification in Social Media (NLP-PRISM) framework, which evaluates vulnerabilities across six dimensions: data collection, preprocessing, visibility, fairness, computational risk, and regulatory compliance. Our analysis shows that transformer models achieve F1-scores ranging from 0.58-0.84, but incur a 1% - 23% drop under privacy-preserving fine-tuning. Using NLP-PRISM, we examine privacy coverage in six NLP tasks: sentiment analysis (16), emotion detection (14), offensive language identification (19), code-mixed processing (39), native language identification (29), and dialect detection (24) revealing substantial gaps in privacy research. We further found a (reduced by 2% - 9%) trade-off in model utility, MIA AUC (membership inference attacks) 0.81, AIA accuracy 0.75 (attribute inference attacks). Finally, we advocate for stronger anonymization, privacy-aware learning, and fairness-driven training to enable ethical NLP in social media contexts.
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