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