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arXiv · 2306.05552

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

Anaelia Ovalle·Mehrab Beikzadeh·Parshan Teimouri·Kai-Wei Chang·Majid Sarrafzadeh
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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. 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.

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Anaelia Ovalle, Mehrab Beikzadeh, Parshan Teimouri, Kai-Wei Chang, Majid Sarrafzadeh. 2023-05-19. ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery. https://arxiv.org/abs/2306.05552

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