language:
- en
license: mit
tags:
- phi
- de-identification
- clinical-nlp
- privacy
- audit
- healthcare
- synthetic
size_categories:
- 1K<n<10K
task_categories:
- token-classification
- text-classification
configs:
- config_name: default
data_files:
- split: train
path: data/train.jsonl
- split: test
path: data/test.jsonl
default: true
phi-audit-trace-benchmark
Author: Venkata Krishna Azith Teja Ganti Part of the ExposureGuard PHI Re-identification Risk Ecosystem
Synthetic clinical text records paired with token-level PHI spans, masking decisions, cryptographic audit hashes, and policy contract metadata. Every record traces the full decision path from raw text to masked output, making it suitable for training and evaluating PHI masking, policy enforcement, and audit-trail verification systems.
Dataset Description
Each record contains a synthetic clinical note generated from templates with randomized PHI values (names, dates, MRNs, addresses, etc.), the masking policy applied based on risk score and consent level, and a SHA-256 audit hash committing the policy decision to the record identity.
This is the only public dataset pairing de-identification decisions with cryptographic audit traces and consent-level policy contracts.
Splits
| Split | Records |
|---|---|
| train | 4,000 |
| test | 1,000 |
Schema
{
"record_id": "AUDIT-000001",
"modality": "text",
"consent_level": "standard",
"cross_modal": false,
"original_text": "Patient John Smith (DOB 1962-03-14, MRN MRN-482910) ...",
"masked_text": "[REDACTED] (DOB [REDACTED], MRN [REDACTED]) ...",
"phi_spans": [
{"phi_type": "NAME", "value": "John Smith", "start": 8, "end": 18},
{"phi_type": "DOB", "value": "1962-03-14", "start": 25, "end": 35}
],
"policy_contract": {
"chosen_policy": "redact",
"risk_score_before": 0.72,
"risk_score_after": 0.072,
"consent_level": "standard",
"modality": "text",
"policy_version": "v1"
},
"audit": {
"audit_hash": "a3f9...",
"timestamp_unix": 1741200000,
"phi_types_detected": ["DOB", "MRN", "NAME"],
"span_count": 3
}
}
Policy Distribution (train)
| Policy | Count |
|---|---|
| pseudo | ~2,160 |
| raw | ~960 |
| redact | ~880 |
PHI Types Covered
NAME, DOB, MRN, ADDRESS, PHONE, EMAIL, SSN, DATE, AGE, LOCATION
Policies Implemented
| Policy | Behavior |
|---|---|
raw |
No masking, minimal consent |
weak |
Generalize dates/ages, bracket other types |
pseudo |
MD5-keyed pseudonymization per span |
redact |
Replace all PHI with [REDACTED] |
synthetic |
Replace with fixed synthetic values |
Related Models
| Model | Role |
|---|---|
| vkatg/exposureguard-policynet | Trained on policy contract features |
| vkatg/dcpg-cross-modal-phi-risk-scorer | Produces risk scores matching this dataset |
| vkatg/exposureguard-synthrewrite-t5 | Downstream consumer of masked records |
Related Datasets
Citation
@misc{ganti2025exposureguard,
title = {ExposureGuard: Cross-Modal PHI Re-identification Risk Scoring with DCPG and Federated CRDT Distillation},
author = {Ganti, Venkata Krishna Azith Teja},
year = {2025},
doi = {10.5281/zenodo.18865882},
howpublished = {\url{https://huggingface.co/vkatg}}
}
License
MIT. Fully synthetic data. Contains no real patient information.