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DP-CARE:在社交媒体帖子中进行心理健康分析的差异性私有分类器

Dimitris Karpontinis¹, Efstathia Soufleri¹

¹Archimedes, Athena Research Center, Athens, Greece.

Frontiers in digital health

|December 29, 2025

概括

我们开发了DP-CARE,这是心理健康NLP模型的隐私保护框架. 它有效地检测压力,同时保护敏感的用户数据,提供实用和道德的解决方案.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
计算精神病学是一种计算精神病学.
保护隐私的机器学习

背景情况:

NLP模型对于检测心理健康状况至关重要,例如来自社交媒体的压力.
变压器模型 (例如,MentalBERT) 显示高性能,但存在存储敏感用户数据的风险.
人们对心理健康NLP中的数据隐私和意外披露存在担忧.

研究的目的:

引入DP-CARE,这是一个新的隐私保护框架,用于心理健康NLP.
为了减轻与训练NLP模型与敏感用户数据相关的隐私风险.
保持高预测性能,同时确保数据保密.

主要方法:

DP-CARE使用一个轻量级分类器连接到一个冷的,域特定的编码器.
该模型使用差别私人AdamW (DP-AdamW) 进行训练,以增强隐私.
这种方法将计算效率与隐私保护放在首位.

主要成果:

在Dreaddit数据集上对DP-CARE进行了评估,用于压力检测.
获得了78.08%的F1竞争得分和88.67%的召回.
在约 ε=3.3 的隐私预算下保持强的表现.

结论:

轻量级,差异化的私人微调是心理健康NLP的实用和伦理方法.
DP-CARE成功地降低了隐私风险,而不会显著影响预测性能.
该框架允许在敏感的心理健康应用中部署NLP系统.

关键词:

在法学士 (LLM) 课程中.不同的隐私差异隐私差异心理健康心理健康隐私隐私隐私隐私隐私隐私压力检测压力检测

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