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

ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery