Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks. In this paper, we take the initiative to protect the user-side privacy associated with online self-disclosure through identification and abstraction. We develop a taxonomy of 19 self-disclosure categories, and curate a large corpus consisting of 4.8K annotated disclosure spans. We then fine-tune a language model for identification, achieving over 75% in Token F$_1$. We further conduct a HCI user study, with 82\% of participants viewing the model positively, highlighting its real world applicability. Motivated by the user feedback, we introduce the task of self-disclosure abstraction. We experiment with both one-span abstraction and three-span abstraction settings, and explore multiple fine-tuning strategies. Our best model can generate diverse abstractions that moderately reduce privacy risks while maintaining high utility according to human evaluation.

通过鉴别和抽象，本文提出保护用户在线自披露隐私的方法，开发了一个包括19个自披露类别的分类方式，并建立了一个由4.8K注释披露跨度组成的大样本，通过微调语言模型，在Token F1上达到75%以上。进一步进行了用户研究，82%的参与者对模型持积极态度，强调其在现实世界中的适用性。在用户反馈的推动下，提出了自披露抽象的任务，尝试了单一抽象和三个抽象设置，并探索了多种微调策略。我们的最佳模型能够生成多样的抽象，可以在适度降低隐私风险的同时保持高度效用，根据人工评估。

减少在线自我披露的隐私风险