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

Abstract-- 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. 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 and find that recommendations are able to be moderately helpful and relevant, even if original user text is not used. We measured cosine similarity after calculating TF-IDF on Language technologies have proven useful in improving P versus NP responses and found an average score of 0.25, mental health outcomes according to scholarly literature [1], indicating some similarity between responses.