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

Authors

• Md Abdur Rahman, University of West Florida
• Md Abdul Barek, University of West Florida
• ABM Kamrul Islam Riad, University of West Florida
• Md Mostafizur Rahman, University of West Florida
• Bajlur Rashid, University of West Florida
• Md Raihan Mia, Marquette University
• Hossain Shahriar, University of West Florida
• Guillermo Francia, University of West Florida
• Fan Wu, Tuskegee University
• Alfredo Cuzzocrea, University of Triste, Italy
• Sheikh Iqbal Ahamed, Marquette UniversityFollow

Document Type

Conference Proceeding

Publication Date

2025

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)

Source ISSN

2836-3795

Original Item ID

DOI: 10.1109/COMPSAC65507.2025.00135

Abstract

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.

Comments

Published as part of the proceedings of the IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC) 2025: 1040-1046. DOI.

Recommended Citation

Rahman, Md Abdur; Barek, Md Abdul; Islam Riad, ABM Kamrul; Rahman, Md Mostafizur; Rashid, Bajlur; Mia, Md Raihan; Shahriar, Hossain; Francia, Guillermo; Wu, Fan; Cuzzocrea, Alfredo; and Ahamed, Sheikh Iqbal, "Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules" (2025). Computer Science Faculty Research and Publications. 124.
https://epublications.marquette.edu/comp_fac/124