用BERT嵌入代码模式，精准分类医疗应用隐私规则
Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules
- 用多语言BERT生成上下文相关代码嵌入
- 逻辑回归准确率达99.95%，全面超越旧方法
- 适合mHealth开发者快速合规，提升应用安全性
尽管mHealth应用开发者有责任保护患者数据并遵守严格的隐私安全要求，但许多人缺乏对HIPAA法规的认知，难以区分不同规则类别。因此，提供针对HIPAA规则模式分类的指导对开发符合Google Play商店安全标准的应用至关重要。本文发现传统Word2Vec在处理代码模式时存在局限性，为此采用多语言BERT（Bidirectional Encoder Representations from Transformers）为数据集属性生成上下文化嵌入以克服问题。我们将BERT应用于数据集，对代码模式进行嵌入，并输入多种机器学习模型。结果表明，该方法显著提升分类性能：逻辑回归达99.95%准确率，支持向量机（99.79%）、随机森林（99.73%）和朴素贝叶斯（95.93%）也均表现优异，优于现有方法。本工作验证了该技术的有效性，展示了其在安全应用开发中的潜力。
原文摘要 · Abstract (English)
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.
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