ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery
This addresses privacy concerns for data-sensitive domains like healthcare, enabling safer use of AI tools, though it is incremental as it builds on existing privacy-preserving methods.
The paper tackles the problem of using ChatGPT for mental health care while preserving user data privacy by proposing a text ambiguation framework, and finds that ChatGPT's recommendations remain moderately helpful and relevant even without the original user text.
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.