dcpg-cross-modal-phi-risk-scorer

DCPG: Cross-Modal PHI Re-Identification Risk Scorer

Patent pending: US provisional filed 2025-07-05 GitHub: phi-exposure-guard

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The problem

Most PHI de-identification tools process one record at a time. A single clinical note might be low risk on its own. But if the same patient shows up in a text note, a voice transcript, and an imaging report within the same time window, the combined signal is enough to re-identify them.

No existing open-source tool tracks that. This one does.

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What it does

DCPG keeps a running exposure state per patient across streaming events. As events arrive from different data modalities, it scores the cumulative re-identification risk and fires a pseudonym rotation when cross-modal co-occurrence pushes risk over a threshold.

The trigger condition is a causal counterfactual check:

baseline_risk < threshold  AND  augmented_risk >= threshold  AND  L(t) > 0

The rotation only fires if the cross-modal linkage bonus L(t) is what caused the threshold crossing, not just any threshold crossing. This avoids false rotations from exposure accumulation alone.

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Supported modalities

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Install

pip install dcpg-standalone
pip install sentence-transformers  # optional, improves text embeddings

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Usage

from dcpg_standalone import DCPGStandalone

scorer = DCPGStandalone()

scorer.add_event("patient_001", "text", "Clinical note with identifiers")
scorer.add_event("patient_001", "asr", "Voice transcript from same encounter")
scorer.add_event("patient_001", "image_proxy", None)

print(scorer.get_risk("patient_001"))
print(scorer.get_pseudonym_version("patient_001"))
print(scorer.full_report("patient_001"))

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Risk formula

R(t) = clip(0.8 * U(t) + 0.2 * D(t) + L(t), 0, 1)

• U(t) = 1 - exp(-0.05 * effective_units)
• D(t) = 0.5 ^ (age_seconds / half_life)
• L(t) = 0.20 for 2-modality co-occurrence, 0.30 for 3+

The 0.8/0.2 weight split was selected by grid search over the MIMIC-III Demo cohort (100 ICU patients), minimizing false trigger rate while maximizing recall of genuine multi-modal co-occurrence windows.

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Benchmark results

MIMIC-III Clinical Database Demo

100 ICU patients, open access via PhysioNet (ODbL license). Each structured table was mapped to a DCPG modality:

MTSamples

5,000 de-identified clinical transcriptions, Apache-2.0. Specialties mapped to modalities by note origin (dictated vs written vs imaging).

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Persistent state

scorer = DCPGStandalone(db_path="./phi_risk_state.db")
scorer.add_event("patient_001", "text", "clinical note")
scorer.close()

# next session
scorer = DCPGStandalone(db_path="./phi_risk_state.db")
print(scorer.get_risk("patient_001"))  # accumulated risk preserved

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Comparison

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Federated use

Graph merging across federated nodes is supported via the CRDT module. See dcpg_crdt.py in the GitHub repo.

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Citation

@software{vkatg2026dcpg,
  author    = {[Venkata Krishna Azith Teja Ganti]},
  title     = {DCPG: Cross-Modal PHI Re-Identification Risk Scorer},
  year      = {2026},
  publisher = {Hugging Face},
  doi       = {10.5281/zenodo.18865882},
  url       = {https://huggingface.co/vkatg/dcpg-cross-modal-phi-risk-scorer}
}

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Disclaimer

Research artifact. Not a certified medical device. Not a substitute for a full compliance pipeline. Users are responsible for HIPAA, GDPR, and any other applicable regulations.

The US provisional patent (filed 2025-07-05) covers the deployed system architecture. The underlying algorithms and risk scoring formulas are freely available for academic research, experimentation, and non-commercial use under the MIT license. If you are building on this work in a research context, no permission is needed beyond standard citation.