README.md

metadata

pretty_name: Streaming PHI Deidentification Benchmark
license: mit
language:
  - en
tags:
  - pii
  - phi
  - de-identification
  - deidentification
  - healthcare
  - healthcare-nlp
  - clinical-text
  - medical-nlp
  - privacy
  - hipaa
  - reinforcement-learning
  - streaming
  - multimodal
  - audit-log
  - synthetic
  - benchmark
  - evaluation
  - synthetic-data
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: audit_log.jsonl
  - config_name: signed
    data_files:
      - split: train
        path: audit_log_signed_adaptive.jsonl
  - config_name: crossmodal
    data_files:
      - split: train
        path: data/crossmodal_train.jsonl

Streaming PHI De-Identification Benchmark

A benchmark dataset for evaluating adaptive de-identification policies on multimodal streaming healthcare data. Every record is fully synthetic. No real patient data, protected health information, or identifiable individuals are present anywhere in this dataset.

This dataset is different from existing PHI benchmarks. Most evaluate masking quality on static, single-modality documents. This dataset captures how re-identification risk accumulates across modalities and time, the same subject appearing across clinical notes, ASR transcripts, imaging proxies, waveform data, and audio metadata over a longitudinal stream.

It supports:

• Benchmarking adaptive vs static de-identification policies
• Studying cross-modal PHI linkage behavior
• Evaluating privacy-utility tradeoffs in streaming systems
• Reproducing results from the associated research implementation

Quick Start

# Install dependencies
# pip install phi-exposure-guard datasets pandas

import pandas as pd
import json

# Load policy benchmark results
df = pd.read_csv("policy_metrics.csv")
print(df)

# Load full audit log
with open("audit_log.jsonl") as f:
    events = [json.loads(line) for line in f]

# Filter adaptive policy events
adaptive = [e for e in events if e["policy_run"] == "adaptive"]

# Filter by modality
text_events = [e for e in events if e["modality"] == "text"]

Or load via the Hugging Face datasets library:

from datasets import load_dataset

# Base audit log (default config)
ds = load_dataset("vkatg/streaming-phi-deidentification-benchmark")

# Signed adaptive audit trail (includes _signature, _record_hash, _chain_ts)
ds_signed = load_dataset("vkatg/streaming-phi-deidentification-benchmark", "signed")

Motivation

Automatic de-identification of healthcare data is critical for safe secondary use in AI research and clinical analytics. Existing PHI datasets have significant limitations for streaming and multimodal evaluation:

• They evaluate each document in isolation with no memory of prior events
• They are restricted to single modalities, usually clinical text only
• Most require data use agreements and are not openly available
• None model cumulative re-identification risk across events and modalities

This dataset was created to fill that gap — providing a reproducible, open benchmark for evaluating exposure-aware and adaptive de-identification strategies in realistic streaming pipelines.

Privacy-Utility Tradeoff

The adaptive policy achieves full utility while keeping leakage close to the redact floor. It only escalates masking strength when cumulative exposure actually justifies it, not on every record by default.

Policy Benchmark Results

Policy	Leak Total	Utility Proxy	Mean Latency (ms)	P90 Latency (ms)
raw	3.03	1.0	0.123	0.154
weak	2.0	0.51	0.141	0.18
pseudo	0.51	1.0	0.159	0.188
redact	0.51	0.51	0.157	0.192
adaptive	0.56	1.0	1.16	1.237

Comparison with Existing PHI Datasets

Dataset	Modality	Access	Real Data	Streaming	Size
i2b2 2014	Text only	DUA required	Yes	No	1,304 records
PhysioNet deid	Text only	DUA required	Yes	No	2,434 notes
This dataset	Text, ASR, Image, Waveform, Audio	Open	No (synthetic)	Yes	1K-10K events

The key distinction is multimodal streaming evaluation. Existing datasets evaluate masking quality on static text records in isolation. This dataset captures how re-identification risk accumulates across modalities and time steps.

Dataset Structure

The primary file is audit_log.jsonl. Each line is one masking decision for one event.

Key Fields

Field	Description
event_id	Event identifier within the stream
patient_key	Synthetic subject identifier
modality	Data modality: text, asr, image_proxy, waveform, audio
policy_run	Which policy was evaluated: raw, weak, pseudo, redact, adaptive
chosen_policy	The policy actually applied
reason	Why that policy was selected
risk	Cumulative re-identification risk score at decision time
localized_remask_trigger	Whether cross-modal synergy triggered pseudonym versioning
latency_ms	Decision latency in milliseconds
leaks_after	Residual PHI leakage after masking
policy_version	Policy version token
decision_blob	Full structured decision record including risk components, DCPG graph state, CMO execution log, and cross-modal matches

Sample Record

{
  "event_id": "evt_0",
  "patient_key": "patient_1",
  "modality": "text",
  "policy_run": "adaptive",
  "chosen_policy": "pseudo",
  "reason": "risk_threshold_exceeded",
  "risk": 0.43,
  "localized_remask_trigger": false,
  "latency_ms": 1.12,
  "leaks_after": 0.0,
  "policy_version": "v1"
}

Modalities

Each event is evaluated across five modalities representing a realistic multimodal healthcare stream:

• text — clinical note proxy
• asr — speech recognition transcript proxy
• image_proxy — imaging metadata proxy
• waveform — physiological monitoring proxy
• audio — audio metadata proxy

Policies

• raw — no masking applied
• weak — minimal token suppression
• pseudo — pseudonymization
• redact — full redaction
• adaptive — risk-governed dynamic policy selection

System Architecture

The dataset was generated by a modular pipeline with the following components:

Module	Description
context_state.py	Per-subject exposure state, persisted via SQLite
controller.py	Risk scoring and adaptive policy selection
dcpg.py	Dynamic Context Persistence Graph — tracks cross-modal identity linkage
dcpg_crdt.py	CRDT-based federated graph merging for distributed deployments
cmo_registry.py	Composable Masking Operator registry and DAG execution
flow_controller.py	DAG-based policy flow controller with audit provenance
rl_agent.py	PPO reinforcement learning agent for adaptive policy control
audit_signing.py	Cryptographic signing and FHIR export of audit records
phi_detector.py	PHI detection and leakage measurement
masking_ops.py	Token-level masking operations per policy

Additional Files

File	Description
policy_metrics.csv	Aggregated per-policy evaluation metrics
latency_summary.csv	Latency distribution per policy
audit_log_signed_adaptive.jsonl	Cryptographically signed adaptive policy audit trail
EXPERIMENT_REPORT.md	Full experiment report with leakage breakdown by modality

Limitations

This dataset is fully synthetic and designed to model structural properties of longitudinal healthcare streams. It does not contain real clinical language, real diagnoses, or real patient records. Models trained or evaluated on this dataset should be validated on real clinical data before deployment. The dataset covers English language proxies only.

Source Code and Reproduction

All data in this dataset was generated by the open-source research implementation:

GitHub: azithteja91/phi-exposure-guard

Hugging Face Space: vkatg/amphi-rl-dpgraph

Colab demo:

To regenerate locally:

git clone https://github.com/azithteja91/phi-exposure-guard.git
cd phi-exposure-guard
pip install -e .
python -m amphi_rl_dpgraph.run_demo

Citation

If you use this dataset in academic or technical work, please cite via the CITATION.cff file in the GitHub repository.

License

MIT

Note on Intended Use

This dataset is intended for privacy research, streaming system design, and reproducible evaluation of de-identification strategies. It is not derived from real clinical data and should not be used as a substitute for validated compliance infrastructure.