KronosXAI/HIPAA-BERT-v0.1

HIPAA-BERT: PII/PHI Column Name Classifier

A fine-tuned BERT model for classifying database column names as PII (Personally Identifiable Information), PHI (Protected Health Information), or Other (O).

Model Details

Property	Value
Developer	KronosX AI Labs
Model Type	BERT + LoRA (text classification)
Base Model	bert-base-uncased
Language	English
Fine-tuning Method	LoRA (Low-Rank Adaptation)
Task	Sequence Classification (3 classes)

Labels

Label	Description	Examples
O	Other/Safe columns	id, created_at, status
PII	Personally Identifiable Info	email, phone_number, address
PHI	Protected Health Info (HIPAA)	diagnosis_code, patient_name, ssn

Training Details

Hyperparameters

Parameter	Value
Learning Rate	1e-3
Batch Size	64
Epochs	10
Weight Decay	0.01
Max Sequence Length	64
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.1
Target Modules	query, value

Training Data

Custom HIPAA-compliant dataset with ~50000+ labeled column names from healthcare databases.

Hardware

• GPU: NVIDIA GPU (Kaggle)
• Mixed Precision: FP16 enabled

Performance Metrics

Metric	Score
Accuracy	~95%+
F1 (weighted)	~94%+
Precision	~93%+
Recall	~94%+

Usage

Installation

pip install transformers torch

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch

Load model

model_name = "KronosXAI/HIPAA-BERT-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name)

Classify column names

columns = ["patient_name", "diagnosis_code", "created_at", "email", "status"] for col in columns: inputs = tokenizer(col, return_tensors="pt", truncation=True, max_length=64) with torch.no_grad(): outputs = model(**inputs) prediction = torch.argmax(outputs.logits, dim=-1).item()

label_map = {0: "O", 1: "PII", 2: "PHI"}
print(f"{col}: {label_map[prediction]}")

Expected Output

patient_name: PHI diagnosis_code: PHI created_at: O email: PII status: O

Intended Use

Primary Use Cases

• Automatic PII/PHI detection in database schemas
• Data privacy compliance audits
• HIPAA compliance automation
• Healthcare data anonymization pipelines

Out-of-Scope

• This model classifies column names, not the actual data content
• Not suitable for classifying free-text or unstructured data
• Should be used as part of a larger compliance workflow, not as sole arbiter

Limitations & Bias

• Trained primarily on English column naming conventions
• May not generalize to non-standard or domain-specific naming patterns
• Should be validated with domain experts before production use

Model Card Authors

Abishek - KronosX AI Labs

Citation

@misc{hipaa-bert-2024, author = {KronosX AI Labs}, title = {HIPAA-BERT: PII/PHI Column Name Classifier}, year = {2026}, url = {https://huggingface.co/KronosXAI/HIPAA-BERT-v0.1} }

Links

• Organization: KronosX AI Labs