# 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

```python
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.
