arXiv 2410.206642024-11-07cs.CRcs.AI

基于大型语言模型嵌入的HIPAA保障规则合规性分类

Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules

Md Abdur Rahman, Md Abdul Barek, ABM Kamrul Islam Riad, Md Mostafizur Rahman, Md Bajlur Rashid, Smita Ambedkar, Md Raihan Miaa, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed

备注：: I am requesting the withdrawal of my paper due to critical issues identified in the methodology/results that may impact its accuracy and reliability. I also plan to make substantial revisions that go beyond minor corrections

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AI总结：

本研究利用多语言BERT嵌入代码模式，结合多种机器学习方法，实现对HIPAA保障规则的高精度分类，逻辑回归准确率达99.95%，优于现有方法。

摘要：

尽管mHealth应用的软件开发人员负责保护患者数据并遵守严格的隐私和安全要求，但其中许多人对HIPAA法规缺乏认识，难以区分HIPAA规则类别。因此，为Google Play Store上安全应用的开发提供HIPAA规则模式分类的指导至关重要。在本工作中，我们识别了传统Word2Vec嵌入在处理代码模式方面的局限性。为解决这一问题，我们采用多语言BERT（来自变换器的双向编码器表示），它为数据集属性提供上下文嵌入以克服这些问题。因此，我们将此BERT应用于我们的数据集以嵌入代码模式，然后将这些嵌入代码用于各种机器学习方法。我们的结果表明，这些模型显著提升了分类性能，其中逻辑回归达到了99.95%的显著准确率。此外，我们还从支持向量机（99.79%）、随机森林（99.73%）和朴素贝叶斯（95.93%）获得了高准确率，优于现有方法。这项工作强调了其有效性，并展示了其在安全应用开发中的潜力。

英文摘要：

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to various machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95\%. Additionally, we obtained high accuracy from Support Vector Machine (99.79\%), Random Forest (99.73\%), and Naive Bayes (95.93\%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。