Applying large language models to sanitize self-disclosure in user-generated content

The rise of e-commerce and social networking platforms has led to an increase in the disclosure of personal health information within user-generated content. This study investigates the application of large language models (LLMs) to detect and sanitize sensitive health data shared by users across platforms such as Amazon, patient.info, and Facebook. We propose a methodology that leverages LLMs to evaluate both the sensitivity of disclosed information and the platform-specific semantics of the content. Through prompt engineering, our method identifies sensitive information and rephrases it to minimize disclosure while preserving content similarity. ChatGPT serves as the LLM in this study due to its versatility. Empirical results suggest that ChatGPT can reliably assign sensitivity scores to user-generated text and generate sanitized versions that effectively preserve the original meaning.