Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks
<|reference_start|>Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks: This study examines integrating EHRs and NLP with large language models (LLMs) to improve healthcare data management and patient care. It focuses on using advanced models to create secure, HIPAA-compliant synthetic patient notes for biomedical research. The study used de-identified and re-identified MIMIC III datasets with GPT-3.5, GPT-4, and Mistral 7B to generate synthetic notes. Text generation employed templates and keyword extraction for contextually relevant notes, with one-shot generation for comparison. Privacy assessment checked PHI occurrence, while text utility was tested using an ICD-9 coding task. Text quality was evaluated with ROUGE and cosine similarity metrics to measure semantic similarity with source notes. Analysis of PHI occurrence and text utility via the ICD-9 coding task showed that the keyword-based method had low risk and good performance. One-shot generation showed the highest PHI exposure and PHI co-occurrence, especially in geographic location and date categories. The Normalized One-shot method achieved the highest classification accuracy. Privacy analysis revealed a critical balance between data utility and privacy protection, influencing future data use and sharing. Re-identified data consistently outperformed de-identified data. This study demonstrates the effectiveness of keyword-based methods in generating privacy-protecting synthetic clinical notes that retain data usability, potentially transforming clinical data-sharing practices. The superior performance of re-identified over de-identified data suggests a shift towards methods that enhance utility and privacy by using dummy PHIs to perplex privacy attacks.<|reference_end|>
@article{chuang2024robust,
    title={Robust Privacy Amidst Innovation with Large Language Models Through a
  Critical Assessment of the Risks},
    author={Yao-Shun Chuang, Atiquer Rahman Sarkar, Yu-Chun Hsu, Noman Mohammed,
  and Xiaoqian Jiang},
    journal={arXiv preprint arXiv:2407.16166},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16166},
    primaryClass={cs.CL}
}