aivanov_CIM/TASK_3_SUMMARY.md

Task 3: PII Protector Implementation - Summary

Completed: Task 3.1 - PIIProtector Class with Hybrid Detection

Implementation Overview

Successfully implemented the PIIProtector class in src/pipeline_mco_pmsi/processors/pii_protector.py with the following features:

Core Functionality

1. Hybrid Detection Approach (Regex + NER)

   • Regex patterns for structured data (dates, NSS, phones, emails, addresses)
   • NER (Named Entity Recognition) support via spaCy for person names
   • Context-based name detection as fallback

2. PII Types Detected

   • Names: Using NER and context patterns (e.g., "Patient Jean Dupont", "M. Martin")
   • Birth Dates: Multiple formats (JJ/MM/AAAA, AAAA-MM-JJ, "15 mars 1960", etc.)
   • NSS (Social Security Numbers): With and without spaces (15 digits)
   • Phone Numbers: Various formats (spaces, dots, dashes, international)
   • Emails: Standard email format
   • Addresses: Street addresses and postal codes

3. Key Methods

   • detect_pii(text): Detects all PII in text, returns list of PIISpan objects
   • anonymize_text(text, pii_spans): Replaces PII with placeholders
   • filter_logs(log_entry): Filters PII from log entries
   • has_pii(text): Checks if text contains PII

4. Design Principles

   • High Recall Preference: Prefers false positives over false negatives to avoid PII leaks
   • Span Merging: Automatically merges overlapping detections
   • Confidence Scoring: Each detection has a confidence score (0.0-1.0)
   • Lazy Loading: NER model loaded only when needed

Test Coverage

Created comprehensive unit tests in tests/test_pii_protector.py:

• 38 unit tests covering:

  • Detection of all PII types with various formats
  • Anonymization functionality
  • Log filtering
  • Edge cases (empty text, overlapping spans, composite names, etc.)

• Test Results: ✅ All 38 tests passing

• Code Coverage: 82% for pii_protector.py module

Requirements Satisfied

✅ Exigence 11.1: Hybrid detection (regex + NER) implemented
✅ Exigence 11.2: PII excluded from logs via filter_logs()
✅ Exigence 11.3: PII excluded from error messages (via anonymization)
✅ Exigence 5.10: Audit logs maintained without PII exposure

Key Features

1. Flexible Regex Patterns

   • Handles multiple date formats (slash, dash, ISO, text)
   • Detects NSS with/without spaces
   • Supports various phone number formats
   • Postal codes and street addresses

2. Smart Name Detection

   • Context-based detection ("Patient", "M.", "Mme", etc.)
   • Optional NER integration with spaCy
   • Handles composite names (Jean-Pierre, Dupont-Martin)

3. Robust Anonymization

   • Replaces PII with clear placeholders ([NOM_ANONYMISÉ], [NSS], etc.)
   • Preserves text structure
   • Handles multiple PII types in same text

4. Conservative Approach

   • High recall to minimize PII leaks
   • Accepts false positives as acceptable trade-off
   • Comprehensive pattern coverage

Files Created

1. src/pipeline_mco_pmsi/processors/pii_protector.py (400+ lines)

   • PIIProtector class
   • PIISpan dataclass
   • Comprehensive regex patterns
   • NER integration support

2. src/pipeline_mco_pmsi/processors/__init__.py

   • Module exports

3. tests/test_pii_protector.py (450+ lines)

   • 38 unit tests
   • 4 test classes covering different aspects
   • Edge case testing

Optional Tasks (Not Implemented)

• Task 3.2: Property-based tests (marked as optional)
• Task 3.3: Additional unit tests for edge cases (marked as optional)

These can be implemented later if needed for more exhaustive testing.

Next Steps

The PII Protector is now ready to be integrated into the pipeline:

• Can be used by the Audit Logger to filter logs
• Can be used to anonymize clinical text before export
• Can be used to validate that no PII leaks into system outputs

Usage Example

from pipeline_mco_pmsi.processors import PIIProtector

# Initialize protector
protector = PIIProtector(use_ner=False)  # or True to enable NER

# Detect PII
text = "Patient Jean Dupont, né le 15/03/1960, NSS 1 60 03 75 123 456 78"
pii_spans = protector.detect_pii(text)

# Anonymize text
anonymized = protector.anonymize_text(text)
# Result: "Patient [NOM_ANONYMISÉ], né le [DATE_NAISSANCE], NSS [NSS]"

# Filter logs
log = "ERROR: Patient Jean Dupont - traitement échoué"
filtered = protector.filter_logs(log)
# Result: "ERROR: Patient [NOM_ANONYMISÉ] - traitement échoué"

# Check for PII
has_pii = protector.has_pii(text)  # Returns True

Conclusion

Task 3.1 successfully completed with a robust, well-tested PII protection system that meets all specified requirements. The implementation follows the conservative approach specified in the requirements, prioritizing high recall to prevent PII leaks.