demo: Test d'anonymisation sur document réel

- Test sur 003_simple_compte_rendu_CRO_23155084.pdf
- 25 PII détectés (4 sur page principale + propagation globale)
- Types: NOM, ADRESSE, CODE_POSTAL, DATE_NAISSANCE
- Validation: AUCUNE FUITE détectée ✓
- Scripts d'analyse: analyze_anonymization_result.py, demo_complete_anonymization.py
- Résultats dans tests/ground_truth/pdfs/anonymized_test/
This commit is contained in:
2026-03-02 10:19:55 +01:00
parent c78f9f415d
commit f61e767ee6
7 changed files with 419 additions and 0 deletions

View File

@@ -0,0 +1,122 @@
#!/usr/bin/env python3
"""
Analyse des résultats d'anonymisation.
"""
import json
from pathlib import Path
from collections import Counter
from evaluation import LeakScanner
def main():
# Fichiers générés
base_name = "003_simple_compte_rendu_CRO_23155084"
output_dir = Path("tests/ground_truth/pdfs/anonymized_test")
audit_path = output_dir / f"{base_name}.audit.jsonl"
redacted_pdf = output_dir / f"{base_name}.redacted_raster.pdf"
text_path = output_dir / f"{base_name}.pseudonymise.txt"
print("="*80)
print("ANALYSE DES RÉSULTATS D'ANONYMISATION")
print("="*80)
print(f"\n📄 Document: {base_name}.pdf")
print(f" Type: Compte-rendu opératoire (CRO)")
# Analyser l'audit
if audit_path.exists():
print(f"\n📊 ANALYSE DE L'AUDIT")
print(f" Fichier: {audit_path.name}")
pii_list = []
with open(audit_path, 'r', encoding='utf-8') as f:
for line in f:
if line.strip():
pii_list.append(json.loads(line))
print(f"\n Total PII détectés: {len(pii_list)}")
# Compter par type
type_counts = Counter(pii['kind'] for pii in pii_list)
print(f"\n Répartition par type:")
for pii_type, count in sorted(type_counts.items(), key=lambda x: -x[1]):
print(f" {pii_type:20s} : {count:3d}")
# Afficher les PII uniques (page 0 uniquement)
page0_pii = [p for p in pii_list if p.get('page') == 0]
if page0_pii:
print(f"\n PII détectés sur la page principale:")
for pii in page0_pii:
original = pii.get('original', '')[:60]
print(f"{pii['kind']:20s} : {original}")
# Afficher les noms extraits (propagation globale)
extracted_names = [p for p in pii_list if p.get('kind') == 'NOM_EXTRACTED']
if extracted_names:
unique_names = set(p['original'] for p in extracted_names)
print(f"\n Noms propagés globalement ({len(unique_names)} uniques):")
for name in sorted(unique_names):
count = sum(1 for p in extracted_names if p['original'] == name)
print(f"{name:20s} : {count} occurrences")
# Afficher le texte anonymisé
if text_path.exists():
print(f"\n📝 TEXTE ANONYMISÉ")
print(f" Fichier: {text_path.name}")
with open(text_path, 'r', encoding='utf-8') as f:
text = f.read()
print(f"\n Extrait (200 premiers caractères):")
print(" " + "-"*76)
lines = text[:200].split('\n')
for line in lines[:5]:
print(f" {line}")
print(" " + "-"*76)
# Scanner les fuites
if redacted_pdf.exists() and audit_path.exists():
print(f"\n🔒 SCAN DE FUITE")
print(f" PDF anonymisé: {redacted_pdf.name}")
scanner = LeakScanner()
leak_report = scanner.scan(redacted_pdf, audit_path)
if leak_report.is_safe:
print(f"\n ✓ DOCUMENT SÛR")
print(f" Aucune fuite détectée")
else:
print(f"\n ✗ ATTENTION - {leak_report.leak_count} fuite(s)")
# Par sévérité
print(f"\n Fuites par sévérité:")
for severity, count in sorted(leak_report.severity_counts.items()):
print(f" {severity:10s} : {count}")
# Détails
print(f"\n Détails des fuites:")
for i, leak in enumerate(leak_report.leaks[:10], 1):
print(f" {i}. [{leak['severity']}] {leak['type']}")
print(f" {leak['message']}")
if leak_report.leak_count > 10:
print(f" ... et {leak_report.leak_count - 10} autres")
print("\n" + "="*80)
print("✨ Analyse terminée")
print("="*80)
print(f"\n💡 Fichiers disponibles:")
print(f" - PDF anonymisé (raster): {redacted_pdf.name}")
print(f" - PDF anonymisé (vector): {base_name}.redacted_vector.pdf")
print(f" - Texte anonymisé: {text_path.name}")
print(f" - Audit complet: {audit_path.name}")
print(f"\n📂 Répertoire: {output_dir}")
print(f"\n🔍 Pour voir le PDF:")
print(f" xdg-open {redacted_pdf}")
if __name__ == "__main__":
main()