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Conference proceeding
Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules
Proceedings: 2025 IEEE 49th Annual Computers, Software, and Applications Conference COMPSAC 2025, pp.1040-1046
IEEE Annual International Computer Software and Applications Conference
Annual Computers, Software, and Applications Conference (COMPSAC), 49th (Toronto, Ontario, Canada, 07/08/2025–07/11/2025)
08/26/2025
Web of Science ID: WOS:001575960000128
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.
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Details
- Title
- Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules
- Publication Details
- Proceedings: 2025 IEEE 49th Annual Computers, Software, and Applications Conference COMPSAC 2025, pp.1040-1046
- Resource Type
- Conference proceeding
- Conference
- Annual Computers, Software, and Applications Conference (COMPSAC), 49th (Toronto, Ontario, Canada, 07/08/2025–07/11/2025)
- Publisher
- IEEE; ieeexplore
- Series
- IEEE Annual International Computer Software and Applications Conference
- Number of pages
- 7
- Grant note
- National Science Foundation (NSF): 1946442, 2433800 National Institutes of Health (NIH): 9R42LM014356-02A1
This research is funded by the National Science Foundation (NSF) and the National Institutes of Health (NIH) (Award #9R42LM014356-02A1). Also, this work is supported by the National Science Foundation under NSF Awards #1946442 and #2433800. Any opinions, findings, recommendations, expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- Copyright
- © 2025 by The Institute of Electrical and Electronics Engineers, Inc.
- Identifiers
- WOS:001575960000128; 99381484295306600
- Academic Unit
- Center for Cybersecurity
- Language
- English