Large language model (LLM)-based AI delegates are increasingly utilized to act on behalf of users, assisting them with a wide range of tasks through conversational interfaces. Despite their advantages, concerns arise regarding the potential risk of privacy leaks, particularly in scenarios involving social interactions. While existing research has focused on protecting privacy by limiting the access of AI delegates to sensitive user information, many social scenarios require disclosing private details to achieve desired outcomes, necessitating a balance between privacy protection and disclosure. To address this challenge, we conduct a pilot study to investigate user preferences for AI delegates across various social relations and task scenarios, and then propose a novel AI delegate system that enables privacy-conscious self-disclosure. Our user study demonstrates that the proposed AI delegate strategically protects privacy, pioneering its use in diverse and dynamic social interactions.

本研究解决了基于大型语言模型的AI代理在社交互动中可能导致的隐私泄露问题。我们提出了一种新颖的AI代理系统，能够在保护隐私的同时实现必要的自我披露，旨在为用户的社交关系和任务场景提供平衡。用户研究表明，该系统在多样化和动态社交互动中有效保护隐私，具有重要的实践意义。

具有双重关注的AI代理：确保隐私与战略性自我披露