""" Generate vkatg/phi-audit-trace-benchmark dataset. Author: Venkata Krishna Azith Teja Ganti Produces synthetic clinical records paired with masking decisions, audit hashes, policy contracts, and pre/post risk scores. Outputs: data/train.jsonl, data/test.jsonl """ import hashlib import json import os import random import time from typing import Dict, List, Tuple # noqa random.seed(42) MODALITIES = ["text", "asr", "image_proxy", "waveform_proxy", "audio_proxy"] POLICIES = ["raw", "weak", "pseudo", "redact", "synthetic"] NAMES = ["John Smith", "Maria Garcia", "David Lee", "Sarah Johnson", "Robert Chen", "Emily Brown", "James Wilson", "Linda Martinez", "Michael Taylor", "Susan Anderson", "Carlos Rivera", "Patricia Moore", "Kevin Zhang", "Angela White", "Thomas Harris"] LOCATIONS = ["Boston Medical Center", "Mayo Clinic", "Johns Hopkins", "Cleveland Clinic", "Stanford Medical", "UCSF Health"] STREETS = ["Oak", "Main", "Elm", "Cedar", "Pine", "Maple", "Birch"] CITIES = ["Springfield", "Riverside", "Fairview", "Lakewood", "Maplewood"] TEMPLATES = [ "Patient {NAME} (DOB {DOB}, MRN {MRN}) presented with chest pain at {LOCATION}.", "Spoke with {NAME} at {PHONE}. Address on file: {ADDRESS}.", "Lab results for {NAME}, {AGE} year old from {LOCATION}, reviewed on {DATE}.", "{NAME} (MRN {MRN}) discharged on {DATE}. Follow up at {LOCATION}.", "Emergency contact for {NAME}: {PHONE}. DOB: {DOB}.", "Patient identified as {NAME}, SSN {SSN}, residing at {ADDRESS}.", "Referral sent for {NAME} (DOB {DOB}) to specialist in {LOCATION}.", "Confirmed identity of {NAME} via MRN {MRN} and DOB {DOB}.", "{NAME} called from {PHONE} reporting symptoms. Age: {AGE}.", "Record updated for {NAME} at {ADDRESS} on {DATE}.", ] SYNTH_MAP = { "NAME": "Alex Morgan", "DOB": "1980-01-01", "MRN": "MRN-000000", "ADDRESS": "100 Generic Ave, Anytown", "PHONE": "(000) 000-0000", "SSN": "000-00-0000", "DATE": "2000-01-01", "AGE": "45", "LOCATION": "General Hospital", "EMAIL": "patient@example.com", } def _rand_phi(rng: random.Random) -> Dict[str, str]: y = rng.randint(1935, 1985) m = rng.randint(1, 12) d = rng.randint(1, 28) return { "NAME": rng.choice(NAMES), "DOB": "%04d-%02d-%02d" % (y, m, d), "MRN": "MRN-%06d" % rng.randint(100000, 999999), "ADDRESS": "%d %s St, %s" % (rng.randint(100, 999), rng.choice(STREETS), rng.choice(CITIES)), "PHONE": "(%03d) %03d-%04d" % (rng.randint(200, 999), rng.randint(100, 999), rng.randint(1000, 9999)), "SSN": "%03d-%02d-%04d" % (rng.randint(100, 999), rng.randint(10, 99), rng.randint(1000, 9999)), "DATE": "20%02d-%02d-%02d" % (rng.randint(0, 23), rng.randint(1, 12), rng.randint(1, 28)), "AGE": str(rng.randint(18, 95)), "LOCATION": rng.choice(LOCATIONS), "EMAIL": "patient%d@example.com" % rng.randint(1000, 9999), } def _render(template: str, phi: Dict[str, str]) -> Tuple[str, List[Dict]]: text = template spans = [] for phi_type, val in phi.items(): ph = "{%s}" % phi_type if ph not in text: continue start = text.index(ph) text = text.replace(ph, val, 1) spans.append({"phi_type": phi_type, "value": val, "start": start, "end": start + len(val)}) spans.sort(key=lambda x: x["start"]) return text, spans def _risk(spans: List[Dict], modality: str, cross_modal: bool) -> float: types = {s["phi_type"] for s in spans} base = min(0.15 * len(spans), 0.80) if "NAME" in types and "DOB" in types: base += 0.10 if cross_modal: base += 0.12 if modality in ("image_proxy", "audio_proxy"): base += 0.05 return round(min(base, 0.99), 4) def _policy(risk: float, consent: str) -> str: if consent == "minimal": return "raw" if risk >= 0.65: return "redact" if risk >= 0.45: return "pseudo" if risk >= 0.25: return "weak" return "raw" def _mask(text: str, spans: List[Dict], policy: str) -> str: if policy == "raw": return text result = text for s in reversed(spans): val = s["value"] pt = s["phi_type"] if policy == "redact": rep = "[REDACTED]" elif policy == "pseudo": rep = "[%s_%s]" % (pt, hashlib.md5(val.encode()).hexdigest()[:6]) elif policy == "weak": if pt == "DOB": rep = val[:7] + "-XX" elif pt == "AGE": a = int(val) rep = "%d-%d" % ((a // 10) * 10, (a // 10) * 10 + 9) else: rep = "[%s]" % pt else: rep = SYNTH_MAP.get(pt, "[SYNTHETIC]") result = result[:s["start"]] + rep + result[s["end"]:] return result def _audit_hash(record_id: str, policy: str, risk: float, spans: List[Dict]) -> str: payload = json.dumps({ "record_id": record_id, "policy": policy, "risk": risk, "phi_types": sorted({s["phi_type"] for s in spans}), }, sort_keys=True) return hashlib.sha256(payload.encode()).hexdigest() RISK_REDUCTION = {"raw": 1.0, "weak": 0.75, "pseudo": 0.50, "redact": 0.10, "synthetic": 0.35} def generate_record(idx: int, rng: random.Random) -> Dict: record_id = "AUDIT-%06d" % idx modality = rng.choice(MODALITIES) consent = rng.choice(["minimal", "standard", "research", "full"]) cross_modal = rng.random() < 0.25 template = rng.choice(TEMPLATES) phi = _rand_phi(rng) text, spans = _render(template, phi) risk_before = _risk(spans, modality, cross_modal) pol = _policy(risk_before, consent) masked = _mask(text, spans, pol) risk_after = round(risk_before * RISK_REDUCTION[pol], 4) audit_hash = _audit_hash(record_id, pol, risk_before, spans) return { "record_id": record_id, "modality": modality, "consent_level": consent, "cross_modal": cross_modal, "original_text": text, "masked_text": masked, "phi_spans": spans, "policy_contract": { "chosen_policy": pol, "risk_score_before": risk_before, "risk_score_after": risk_after, "consent_level": consent, "modality": modality, "policy_version": "v1", }, "audit": { "audit_hash": audit_hash, "timestamp_unix": int(time.time()) - rng.randint(0, 86400 * 30), "phi_types_detected": sorted({s["phi_type"] for s in spans}), "span_count": len(spans), }, } def main(): os.makedirs("data", exist_ok=True) rng = random.Random(42) train = [generate_record(i, rng) for i in range(4000)] test = [generate_record(i, rng) for i in range(4000, 5000)] with open("data/train.jsonl", "w") as f: for r in train: f.write(json.dumps(r) + "\n") with open("data/test.jsonl", "w") as f: for r in test: f.write(json.dumps(r) + "\n") print("train: %d test: %d" % (len(train), len(test))) pol_dist = {} for r in train: p = r["policy_contract"]["chosen_policy"] pol_dist[p] = pol_dist.get(p, 0) + 1 print("policy dist:", pol_dist) risks = [r["policy_contract"]["risk_score_before"] for r in train] print("avg risk before: %.3f" % (sum(risks) / len(risks))) print("avg risk after: %.3f" % (sum(r["policy_contract"]["risk_score_after"] for r in train) / len(train))) if __name__ == "__main__": main()