Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks

• URL: http://arxiv.org/abs/2407.16166v1
• Date: Tue, 23 Jul 2024 04:20:14 GMT
• Title: Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks
• Authors: Yao-Shun Chuang, Atiquer Rahman Sarkar, Noman Mohammed, Xiaoqian Jiang,
• Abstract summary: 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.
• Score: 8.506138767850773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling [6.193782515824411]
  We present a system that generates synthetic free-text medical records using Masked Language Modeling (MLM) Our system is designed to preserve the critical information of the records while introducing significant diversity and minimizing re-identification risk.
  arXiv  Detail & Related papers  (2024-09-15T19:11:01Z)
• Controllable Synthetic Clinical Note Generation with Privacy Guarantees [7.1366477372157995]
  In this paper, we introduce a novel method to "clone" datasets containing Personal Health Information (PHI) Our approach ensures that the cloned datasets retain the essential characteristics and utility of the original data without compromising patient privacy. We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets.
  arXiv  Detail & Related papers  (2024-09-12T07:38:34Z)
• Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data [51.41288763521186]
  Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. RAG systems may face severe privacy risks when retrieving private data. We propose using synthetic data as a privacy-preserving alternative for the retrieval data.
  arXiv  Detail & Related papers  (2024-06-20T22:53:09Z)
• Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records [1.9749268648715583]
  This research compares state-of-the-art GAN-based models for synthetic data generation to generate time-series synthetic medical records of dementia patients. Our experiments indicate the superiority of the privacy-preserving GAN (PPGAN) model over other models regarding privacy preservation.
  arXiv  Detail & Related papers  (2024-02-21T10:24:34Z)
• De-identification is not always enough [9.292345527034348]
  We show that de-identification of real clinical notes does not protect records against a membership inference attack. When synthetically generated notes closely match the performance of real data, they also exhibit similar privacy concerns to the real data.
  arXiv  Detail & Related papers  (2024-01-31T21:14:01Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4 [80.36535668574804]
  We develop a novel GPT4-enabled de-identification framework (DeID-GPT") Our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification.
  arXiv  Detail & Related papers  (2023-03-20T11:34:37Z)
• Does Synthetic Data Generation of LLMs Help Clinical Text Mining? [51.205078179427645]
  We investigate the potential of OpenAI's ChatGPT to aid in clinical text mining. We propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data. Our method has resulted in significant improvements in the performance of downstream tasks.
  arXiv  Detail & Related papers  (2023-03-08T03:56:31Z)
• Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe [32.63295550058343]
  We show that a simple and practical recipe in the text domain is effective in generating useful synthetic text with strong privacy protection. Our method produces synthetic text that is competitive in terms of utility with its non-private counterpart.
  arXiv  Detail & Related papers  (2022-10-25T21:21:17Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.