Large language models have been useful in expanding mental health care delivery. ChatGPT, in particular, has gained popularity for its ability to generate human-like dialogue. However, data-sensitive domains -- including but not limited to healthcare -- face challenges in using ChatGPT due to privacy and data-ownership concerns. To enable its utilization, we propose a text ambiguation framework that preserves user privacy. We ground this in the task of addressing stress prompted by user-provided texts to demonstrate the viability and helpfulness of privacy-preserved generations. Our results suggest that chatGPT recommendations are still able to be moderately helpful and relevant, even when the original user text is not provided.

提出了一个文本模糊框架，以解决数据敏感领域如医疗保健领域因隐私和数据所有权问题使用ChatGPT遇到的障碍，通过处理用户提供的文本以发现和减轻压力，展示隐私保护生成的可行性和帮助性。我们的结果表明，即使没有提供原始用户文本，ChatGPT的推荐仍然有一定的帮助和相关性。

ChatGPT为我们所用：通过对话文本模糊化保护数据隐私，以扩展心理卫生保健的投递