137 lines
4.8 KiB
Markdown
137 lines
4.8 KiB
Markdown
# Task 3: PII Protector Implementation - Summary
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## Completed: Task 3.1 - PIIProtector Class with Hybrid Detection
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### Implementation Overview
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Successfully implemented the `PIIProtector` class in `src/pipeline_mco_pmsi/processors/pii_protector.py` with the following features:
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#### Core Functionality
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1. **Hybrid Detection Approach (Regex + NER)**
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- Regex patterns for structured data (dates, NSS, phones, emails, addresses)
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- NER (Named Entity Recognition) support via spaCy for person names
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- Context-based name detection as fallback
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2. **PII Types Detected**
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- **Names**: Using NER and context patterns (e.g., "Patient Jean Dupont", "M. Martin")
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- **Birth Dates**: Multiple formats (JJ/MM/AAAA, AAAA-MM-JJ, "15 mars 1960", etc.)
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- **NSS (Social Security Numbers)**: With and without spaces (15 digits)
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- **Phone Numbers**: Various formats (spaces, dots, dashes, international)
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- **Emails**: Standard email format
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- **Addresses**: Street addresses and postal codes
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3. **Key Methods**
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- `detect_pii(text)`: Detects all PII in text, returns list of PIISpan objects
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- `anonymize_text(text, pii_spans)`: Replaces PII with placeholders
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- `filter_logs(log_entry)`: Filters PII from log entries
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- `has_pii(text)`: Checks if text contains PII
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4. **Design Principles**
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- **High Recall Preference**: Prefers false positives over false negatives to avoid PII leaks
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- **Span Merging**: Automatically merges overlapping detections
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- **Confidence Scoring**: Each detection has a confidence score (0.0-1.0)
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- **Lazy Loading**: NER model loaded only when needed
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### Test Coverage
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Created comprehensive unit tests in `tests/test_pii_protector.py`:
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- **38 unit tests** covering:
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- Detection of all PII types with various formats
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- Anonymization functionality
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- Log filtering
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- Edge cases (empty text, overlapping spans, composite names, etc.)
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- **Test Results**: ✅ All 38 tests passing
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- **Code Coverage**: 82% for pii_protector.py module
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### Requirements Satisfied
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✅ **Exigence 11.1**: Hybrid detection (regex + NER) implemented
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✅ **Exigence 11.2**: PII excluded from logs via `filter_logs()`
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✅ **Exigence 11.3**: PII excluded from error messages (via anonymization)
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✅ **Exigence 5.10**: Audit logs maintained without PII exposure
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### Key Features
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1. **Flexible Regex Patterns**
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- Handles multiple date formats (slash, dash, ISO, text)
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- Detects NSS with/without spaces
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- Supports various phone number formats
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- Postal codes and street addresses
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2. **Smart Name Detection**
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- Context-based detection ("Patient", "M.", "Mme", etc.)
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- Optional NER integration with spaCy
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- Handles composite names (Jean-Pierre, Dupont-Martin)
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3. **Robust Anonymization**
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- Replaces PII with clear placeholders ([NOM_ANONYMISÉ], [NSS], etc.)
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- Preserves text structure
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- Handles multiple PII types in same text
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4. **Conservative Approach**
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- High recall to minimize PII leaks
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- Accepts false positives as acceptable trade-off
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- Comprehensive pattern coverage
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### Files Created
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1. `src/pipeline_mco_pmsi/processors/pii_protector.py` (400+ lines)
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- PIIProtector class
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- PIISpan dataclass
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- Comprehensive regex patterns
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- NER integration support
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2. `src/pipeline_mco_pmsi/processors/__init__.py`
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- Module exports
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3. `tests/test_pii_protector.py` (450+ lines)
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- 38 unit tests
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- 4 test classes covering different aspects
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- Edge case testing
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### Optional Tasks (Not Implemented)
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- **Task 3.2**: Property-based tests (marked as optional)
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- **Task 3.3**: Additional unit tests for edge cases (marked as optional)
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These can be implemented later if needed for more exhaustive testing.
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### Next Steps
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The PII Protector is now ready to be integrated into the pipeline:
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- Can be used by the Audit Logger to filter logs
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- Can be used to anonymize clinical text before export
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- Can be used to validate that no PII leaks into system outputs
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### Usage Example
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```python
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from pipeline_mco_pmsi.processors import PIIProtector
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# Initialize protector
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protector = PIIProtector(use_ner=False) # or True to enable NER
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# Detect PII
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text = "Patient Jean Dupont, né le 15/03/1960, NSS 1 60 03 75 123 456 78"
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pii_spans = protector.detect_pii(text)
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# Anonymize text
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anonymized = protector.anonymize_text(text)
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# Result: "Patient [NOM_ANONYMISÉ], né le [DATE_NAISSANCE], NSS [NSS]"
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# Filter logs
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log = "ERROR: Patient Jean Dupont - traitement échoué"
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filtered = protector.filter_logs(log)
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# Result: "ERROR: Patient [NOM_ANONYMISÉ] - traitement échoué"
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# Check for PII
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has_pii = protector.has_pii(text) # Returns True
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```
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## Conclusion
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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.
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