multimodal-phi-masking-benchmark

10,000 synthetic clinical records paired with token-level PHI spans, masking decisions, cryptographic audit hashes, RL reward signals, and leakage scores. Five configs covering text, ASR, imaging, waveform, and audio modalities. The only public dataset pairing PHI masking decisions with FHIR R4 audit trails, RL reward signals, and a formally modeled adversarial evasion scenario.

from datasets import load_dataset

ds      = load_dataset("vkatg/multimodal-phi-masking-benchmark")
pairs   = load_dataset("vkatg/multimodal-phi-masking-benchmark", "multimodal_pairs")
signed  = load_dataset("vkatg/multimodal-phi-masking-benchmark", "signed")
fhir    = load_dataset("vkatg/multimodal-phi-masking-benchmark", "fhir")
adv     = load_dataset("vkatg/multimodal-phi-masking-benchmark", "adversarial")

---

Why this dataset exists

Every PHI de-identification dataset I could find is text-only, single-document, and has no model of how masking decisions were made. You get the original text, the redacted text, and maybe some span annotations. That's useful for training a PHI detector. It's not useful for training or evaluating a masking policy, studying audit traceability, or testing cross-modal re-identification risk.

This dataset was built to fill those gaps. Each record includes the full decision chain: what PHI was detected, what risk score it produced, what policy was selected and why, how much PHI leaked through after masking, and what RL reward the decision would have earned. The multimodal pairs config adds paired records across two modalities for the same synthetic patient, which is the kind of data needed to study cross-modal linkage and re-identification risk accumulation.

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Configs

default (5,000 records: 4,000 train / 1,000 test)

Synthetic clinical records across all five modalities with full masking decision traces.

Modality	Count
text	785
waveform_proxy	834
asr	794
image_proxy	790
audio_proxy	797

Policy	Count
pseudo	1,653
raw	1,016
synthetic	957
redact	374

PHI types covered: NAME (4,000), LOCATION (1,974), MRN (1,646), DATE (1,502), DOB (1,431), AGE (889), PHONE (755), ADDRESS (453), SSN (236).

Average risk before masking: 0.607. Average leakage after masking: 0.262. Average RL reward: 0.261.

multimodal_pairs (4,000 records, 2,000 patients)

Paired records for the same synthetic patient across two different modalities. Both records share the same PHI values, making this config directly useful for studying cross-modal linkage and re-identification risk when the same patient appears in multiple data streams.

Top modality pairs: asr+audio_proxy (450), asr+image_proxy (420), image_proxy+text (402), image_proxy+waveform_proxy (398), audio_proxy+waveform_proxy (396).

Each record includes a linkage field showing shared PHI types and the cross-modal risk nudge (+0.12) that would apply if a system tracked exposure across both records.

signed (4,000 records)

Same as the default train split, with ECDSA-style signature, SHA-256 record hash, and chain timestamp added per record. Useful for evaluating audit trail verification systems and compliance tooling.

fhir (4,000 records)

Default train split exported as FHIR R4 AuditEvent resources. Each record includes chosen policy, risk scores before and after masking, consent level, leakage score, RL reward, and audit hash in the entity.detail array. The only public de-identification dataset in FHIR format.

adversarial (1,000 records)

Records where PHI is deliberately fragmented to stay sub-threshold individually (avg individual risk: 0.363) but would trigger escalation when cross-modal linkage is detected (avg combined risk: 0.483, avg risk delta: +0.12). 463 of 1,000 records have would_trigger_cross_modal: true.

This config models the sub-threshold probing attack described in the phi-exposure-guard system: an attacker who spaces PHI to avoid individual record thresholds while accumulating linkage signal across modalities.

---

Schema

default / multimodal_pairs / signed / adversarial

{
  "record_id": "AUDIT-000001",
  "modality": "asr",
  "consent_level": "standard",
  "cross_modal": true,
  "original_text": "John Smith date of birth 1962-03-14 MRN MRN-482910 presenting at Mayo Clinic",
  "masked_text": "[NAME_a3f9b2] date of birth [DOB_c81d4e] MRN [MRN_7f2a91] presenting at [LOCATION_3d8c1a]",
  "phi_spans": [
    {"phi_type": "NAME", "value": "John Smith", "start": 0, "end": 10},
    {"phi_type": "DOB",  "value": "1962-03-14", "start": 24, "end": 34}
  ],
  "policy_contract": {
    "chosen_policy": "pseudo",
    "risk_score_before": 0.72,
    "risk_score_after": 0.072,
    "consent_level": "standard",
    "modality": "asr",
    "policy_version": "v1"
  },
  "audit": {
    "audit_hash": "a3f9c1...",
    "timestamp_unix": 1741200000,
    "phi_types_detected": ["DOB", "LOCATION", "MRN", "NAME"],
    "span_count": 4
  },
  "metrics": {
    "leakage_score": 0.0,
    "rl_reward": 0.312
  }
}

The adversarial config adds:

"adversarial": {
  "individual_risk": 0.38,
  "combined_risk": 0.50,
  "risk_delta": 0.12,
  "evasion_strategy": "sub_threshold_fragmentation",
  "would_trigger_cross_modal": true
}

The multimodal_pairs config adds:

"pair_id": "PAIR-00001",
"patient_idx": 1,
"paired_with": "image_proxy",
"linkage": {
  "shared_phi_types": ["DOB", "MRN", "NAME"],
  "cross_modal_risk_nudge": 0.12
}

The signed config adds:

"_signature": "a3f9c1d2...",
"_record_hash": "7badcf09...",
"_chain_ts": 1741200001.234

fhir

Standard FHIR R4 AuditEvent resource with masking-specific fields in entity.detail.

---

What you can use this for

PHI detection and masking: the default split has span-level annotations across 9 PHI types and 5 modalities. Covers text, ASR transcripts, imaging metadata, waveform headers, and audio proxy records with modality-appropriate templates.

Masking policy learning: the policy_contract, metrics.leakage_score, and metrics.rl_reward fields give you everything needed for offline RL training or supervised policy classification without running the full adaptive controller.

Cross-modal re-identification research: the multimodal_pairs config is the only public dataset with paired cross-modal clinical records sharing the same patient PHI. Directly applicable to studying how re-identification risk accumulates when the same patient appears across multiple data streams.

Adversarial robustness evaluation: the adversarial config gives you 1,000 records specifically designed to test whether a system can detect sub-threshold evasion via cross-modal linkage. 463 of them demonstrate the evasion being caught.

Compliance tooling: the signed and fhir configs are ready for audit trail verification and FHIR-compatible pipeline testing.

---

Policies

Policy	Behavior
raw	No masking
weak	Generalize dates and ages, bracket other PHI types
synthetic	Replace with fixed synthetic values (Alex Morgan, MRN-000000, etc.)
pseudo	MD5-keyed pseudonymization per span
redact	Replace all PHI with [REDACTED]

Policy is selected based on risk score and consent level. minimal consent always produces raw. Above that, thresholds are: raw below 0.20, weak 0.20-0.35, synthetic 0.35-0.55, pseudo 0.55-0.75, redact above 0.75.

---

Related

• phi-exposure-guard: the adaptive masking system this dataset is built around
• streaming-phi-deidentification-benchmark: event-level adaptive masking traces with crossmodal and adversarial scenarios
• dag-remediation-traces: DAG planning traces for post-escalation remediation
• dcpg-cross-modal-phi-risk-scorer: the risk scoring model that produces the risk fields in this dataset

---

Citation

@dataset{multimodal_phi_masking_benchmark,
  title  = {Multimodal PHI Masking Benchmark},
  author = {Ganti, Venkata Krishna Azith Teja},
  doi    = {10.5281/zenodo.18865882},
  url    = {https://huggingface.co/datasets/vkatg/multimodal-phi-masking-benchmark}
}

MIT License. Fully synthetic data. No real patient information anywhere in this dataset.