LLM BERT Model for HIPAA-Sensitive Database Fields Classification

This repository hosts a fine-tuned BERT-base model that classifies database column names as either PHI HIPAA-sensitive (e.g., birthDate, ssn, address) or non-sensitive (e.g., color, food, country).

Use this model for:

Masking PHI data fields before sharing database to avoid HIPAA compliance
Preprocessing before data anonymization
Identifying patient's sensitive data fields in a dataset before training an AI model
Enhancing security in healthcare and mHealth applications

🧠 Model Info

Base Model: bert-base-uncased
Task: Binary classification (PHI HIPAA Sensitive vs Non-sensitive)
Trained On: GAN generated Synthetic and real-world column name examples
Framework: Hugging Face Transformers
Model URL: https://huggingface.co/barek2k2/bert_hipaa_sensitive_db_schema

🚀 Usage Example (End-to-End)

1. Install Requirements

pip install torch transformers

2. Example

import torch
from transformers import BertTokenizer, BertForSequenceClassification

# Load model and tokenizer
model = BertForSequenceClassification.from_pretrained("barek2k2/bert_hipaa_sensitive_db_schema")
tokenizer = BertTokenizer.from_pretrained("barek2k2/bert_hipaa_sensitive_db_schema")
model.eval()

# Example column names
texts = ["birthDate", "country", "jwtToken", "color"]

# Tokenize input
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=1)

# Display results
for text, pred in zip(texts, predictions):
    label = "Sensitive" if pred.item() == 1 else "Non-sensitive"
    print(f"{text}: {label}")

3. Output

birthDate: Sensitive
country: Non-sensitive
jwtToken: Sensitive
color: Non-sensitive

In the healthcare industry, safeguarding sensitive patient data is of utmost importance, particularly when developing and maintaining software systems that involve database sharing. The Health Insurance Portability and Accountability Act (HIPAA) mandates strict regulations to ensure the privacy and security of Protected Health Information (PHI). Healthcare organizations must comply with these regulations to prevent unauthorized access, breaches, and potential legal consequences. However, ensuring HIPAA compliance becomes a complex challenge when databases are shared among multiple teams for debugging, development, and testing purposes. This research work proposes a novel approach that uses BERT based LLM for identifying sensitive database columns into the database schema in order to avoid PHI HIPAA violation.

Disclaimer

This LLM model is fine-tuned with synthetic dataset(~50K) and is provided for research and educational purposes only. Always verify compliance before using in production environments.

📊 Model Performance Analysis

Table 1: Changing hyperparameters and results

Step	Learning Rate	Batch Size	Epoch	Weight Decay	Precision	Recall	F1 Score	Accuracy
1	0	16	1	0.001	0.0000	0.0000	0.0000	36.78%
2	1e-1	16	1	0.001	0.6321	1.0000	0.7746	63.21%
3	1e-1	32	1	0.001	0.6321	1.0000	0.7746	63.21%
4	1e-1	32	2	0.001	0.6321	1.0000	0.7746	63.21%
5	1e-1	32	3	0.001	0.6321	1.0000	0.7746	63.21%
6	1e-1	32	3	0.01	0.6321	1.0000	0.7746	63.21%
7	2e-1	32	4	0.01	0.6321	1.0000	0.7746	63.21%
8	3e-4	32	4	0.01	0.6331	0.9982	0.7748	63.32%
9	2e-4	32	4	0.01	0.9908	0.9730	0.9818	97.72%
10	1e-5	32	4	0.01	0.9964	0.9928	0.9946	99.31%
11	1e-5	32	5	0.01	0.9964	0.9928	0.9946	99.31%
12	1e-5	16	5	0.01	1.0000	0.9964	0.9982	99.72%
13	1e-5	16	5	0.1	1.0000	0.9946	0.9973	99.65%
14	1e-5	32	5	0.1	1.0000	0.9946	0.9973	99.65%
15	1e-5	32	5	1.0	0.9964	0.9946	0.9946	99.54%
16	1e-6	32	5	1.0	0.8342	0.9153	0.8729	83.15%

Limitations

One of the main limitations of this work is the use of a synthetic dataset instead of real-world data to fine-tune and train the AI models. Although the dataset was carefully checked for accuracy, it may not fully reflect the complexity and diversity of actual healthcare records.

👤 Author

MD Abdul Barek
PhD student & GRA @ Intelligent Systems and Robotics

🏫 University of West Florida, Florida, USA
📧 [email protected]
📧 [email protected]
🔗 Hugging Face Profile

Advisor:
Dr. Hakki Erhan Sevil
Associate Professor Intelligent Systems and Robotics, University of West Florida
📧 [email protected]

Supervisor:
Dr. Guillermo Francia III
Director, Research and Innovation, Center for Cybersecurity, University of West Florida
📧 [email protected]

Co-Supervisor:
Dr. Hossain Shahriar
Associate Director and Professor, Center for Cybersecurity,
University of West Florida
📧 [email protected]

barek2k2
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bert_hipaa_sensitive_db_schema