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https://github.com/PlatypusPus/MushroomEmpire.git
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feat: Add JSON explainability reports with Nordic PII detection
- Convert reports from text to structured JSON format - Add simple and detailed explainability report types - Implement automatic Nordic-specific entity detection (FI, SE, NO, DK) - Include Nordic regulatory compliance information (Finnish DPA, Swedish IMY, etc.) - Add custom JSON serialization for numpy types - Update test suite with Nordic PII test cases - Enhanced cleaning_config with Nordic entities (FI_PERSONAL_ID, SE_PERSONAL_ID, NO_PERSONAL_ID, DK_PERSONAL_ID, FI_KELA_ID, FI_BUSINESS_ID)
This commit is contained in:
589
cleaning.py
589
cleaning.py
@@ -12,7 +12,7 @@ from typing import Dict, List, Tuple, Optional, Any
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from collections import defaultdict
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try:
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from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
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from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern
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from presidio_analyzer.nlp_engine import NlpEngineProvider
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from presidio_anonymizer import AnonymizerEngine
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from presidio_anonymizer.entities import OperatorConfig
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@@ -22,6 +22,21 @@ except ImportError:
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print("Warning: Presidio not installed. Run: pip install presidio-analyzer presidio-anonymizer")
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def convert_to_json_serializable(obj):
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"""Convert numpy types to JSON-serializable Python types"""
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if isinstance(obj, np.integer):
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return int(obj)
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elif isinstance(obj, np.floating):
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return float(obj)
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elif isinstance(obj, np.ndarray):
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return obj.tolist()
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elif isinstance(obj, dict):
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return {key: convert_to_json_serializable(value) for key, value in obj.items()}
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elif isinstance(obj, list):
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return [convert_to_json_serializable(item) for item in obj]
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return obj
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class CleaningConfig:
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"""Configuration for data cleaning strategies"""
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@@ -120,7 +135,7 @@ class DataCleaner:
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)
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def _init_presidio(self):
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"""Initialize Presidio analyzer and anonymizer engines"""
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"""Initialize Presidio analyzer and anonymizer engines with Nordic recognizers"""
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# Create NLP engine configuration
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configuration = {
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"nlp_engine_name": "spacy",
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@@ -132,11 +147,18 @@ class DataCleaner:
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provider = NlpEngineProvider(nlp_configuration=configuration)
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nlp_engine = provider.create_engine()
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# Create analyzer with NLP engine
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self.analyzer = AnalyzerEngine(nlp_engine=nlp_engine)
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# Create registry and add Nordic recognizers
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registry = RecognizerRegistry()
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registry.load_predefined_recognizers(nlp_engine=nlp_engine)
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# Add Nordic-specific recognizers
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self._add_nordic_recognizers(registry)
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# Create analyzer with custom registry
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self.analyzer = AnalyzerEngine(registry=registry, nlp_engine=nlp_engine)
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self.anonymizer = AnonymizerEngine()
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print("✓ Presidio engines initialized successfully")
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print("✓ Presidio engines initialized with Nordic PII recognizers")
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except Exception as e:
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# Fallback to default configuration if spaCy model not available
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print(f"Warning: Could not load spaCy model, using default configuration: {e}")
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@@ -144,6 +166,90 @@ class DataCleaner:
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self.analyzer = AnalyzerEngine()
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self.anonymizer = AnonymizerEngine()
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def _add_nordic_recognizers(self, registry: RecognizerRegistry):
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"""Add custom recognizers for Nordic national IDs and identifiers"""
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# Finnish Henkilötunnus (HETU) - Format: DDMMYY(+/-)NNNC
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fi_hetu_pattern = Pattern(
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name="finnish_hetu_pattern",
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regex=r"\b\d{6}[+\-A]\d{3}[0-9A-FHJ-NPR-Y]\b",
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score=0.95
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)
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fi_hetu_recognizer = PatternRecognizer(
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supported_entity="FI_PERSONAL_ID",
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patterns=[fi_hetu_pattern],
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context=["henkilötunnus", "hetu", "personal", "identity", "id"]
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)
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registry.add_recognizer(fi_hetu_recognizer)
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# Swedish Personnummer - Format: YYYYMMDD-NNNN or YYMMDD-NNNN
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se_personnummer_pattern = Pattern(
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name="swedish_personnummer_pattern",
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regex=r"\b\d{6}[-+]?\d{4}\b",
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score=0.90
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)
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se_personnummer_recognizer = PatternRecognizer(
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supported_entity="SE_PERSONAL_ID",
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patterns=[se_personnummer_pattern],
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context=["personnummer", "personal", "identity", "swedish", "id"]
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)
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registry.add_recognizer(se_personnummer_recognizer)
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# Norwegian Fødselsnummer - Format: DDMMYY NNNNN
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no_fodselsnummer_pattern = Pattern(
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name="norwegian_fodselsnummer_pattern",
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regex=r"\b\d{6}\s?\d{5}\b",
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score=0.90
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)
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no_fodselsnummer_recognizer = PatternRecognizer(
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supported_entity="NO_PERSONAL_ID",
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patterns=[no_fodselsnummer_pattern],
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context=["fødselsnummer", "fodselsnummer", "personal", "identity", "norwegian", "id"]
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)
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registry.add_recognizer(no_fodselsnummer_recognizer)
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# Danish CPR-nummer - Format: DDMMYY-NNNN
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dk_cpr_pattern = Pattern(
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name="danish_cpr_pattern",
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regex=r"\b\d{6}-?\d{4}\b",
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score=0.90
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)
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dk_cpr_recognizer = PatternRecognizer(
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supported_entity="DK_PERSONAL_ID",
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patterns=[dk_cpr_pattern],
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context=["cpr", "cpr-nummer", "personal", "identity", "danish", "id"]
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)
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registry.add_recognizer(dk_cpr_recognizer)
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# Finnish Business ID (Y-tunnus) - Format: NNNNNNN-N
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fi_ytunnus_pattern = Pattern(
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name="finnish_ytunnus_pattern",
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regex=r"\b\d{7}-\d\b",
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score=0.85
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)
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fi_ytunnus_recognizer = PatternRecognizer(
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supported_entity="FI_BUSINESS_ID",
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patterns=[fi_ytunnus_pattern],
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context=["y-tunnus", "ytunnus", "business", "company", "organization"]
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)
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registry.add_recognizer(fi_ytunnus_recognizer)
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# Finnish Kela ID - Format varies
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fi_kela_pattern = Pattern(
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name="finnish_kela_pattern",
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regex=r"\bKELA[-\s]?\d{6,10}\b",
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score=0.85
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)
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fi_kela_recognizer = PatternRecognizer(
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supported_entity="FI_KELA_ID",
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patterns=[fi_kela_pattern],
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context=["kela", "social", "security", "benefit", "insurance"]
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)
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registry.add_recognizer(fi_kela_recognizer)
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print(" ✓ Added Nordic recognizers: FI_PERSONAL_ID, SE_PERSONAL_ID, NO_PERSONAL_ID, DK_PERSONAL_ID")
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print(" ✓ Added Finnish identifiers: FI_BUSINESS_ID, FI_KELA_ID")
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def clean(
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self,
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risky_features: Optional[List[str]] = None,
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@@ -529,6 +635,8 @@ class DataCleaner:
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'presidio_version': 'enabled' if PRESIDIO_AVAILABLE else 'disabled'
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},
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'summary': {
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'total_rows': len(self.df),
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'total_columns': len(self.df.columns),
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'columns_removed': [],
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'columns_anonymized': [],
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'total_cells_affected': 0
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@@ -662,6 +770,477 @@ class DataCleaner:
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print(f"✓ Audit report saved to: {output_path}")
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return output_path
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def generate_simple_report(self, audit_report: Dict, dataset_name: str = "dataset") -> Dict:
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"""
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Generate a simple executive summary report in JSON format
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Args:
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audit_report: Audit report from clean() method
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dataset_name: Name of the dataset for the report
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Returns:
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Simple report as dictionary (JSON-serializable)
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"""
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summary = audit_report['summary']
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removed_cols = summary['columns_removed']
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anon_cols = summary['columns_anonymized']
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total_risky = len(removed_cols) + len(anon_cols)
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orig_cols = summary['total_columns']
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clean_cols = orig_cols - len(removed_cols)
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total_rows = summary['total_rows']
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# Detect Nordic-specific entities
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nordic_entities = ['FI_PERSONAL_ID', 'SE_PERSONAL_ID', 'NO_PERSONAL_ID', 'DK_PERSONAL_ID', 'FI_KELA_ID', 'FI_BUSINESS_ID']
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has_nordic = any(entity in str(audit_report['details']) for entity in nordic_entities)
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# Build columns summary
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columns_summary = []
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# Removed columns
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for col in removed_cols:
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detail = audit_report['details'].get(col, {})
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columns_summary.append({
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"column_name": col,
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"risk_level": "CRITICAL",
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"action_taken": "REMOVED",
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"entity_types": detail.get('entity_types_found', ['PII']),
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"reason": detail.get('reason', 'High-risk PII detected')
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})
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# Anonymized columns
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for col in anon_cols:
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detail = audit_report['details'].get(col, {})
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columns_summary.append({
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"column_name": col,
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"risk_level": "MEDIUM",
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"action_taken": "ANONYMIZED",
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"strategies": detail.get('strategies_applied', ['anonymized']),
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"entity_types": detail.get('entity_types_found', ['PII']),
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"rows_affected": detail.get('num_affected_rows', 0),
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"percentage_affected": detail.get('percentage_affected', '0%')
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})
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# Build simple report JSON
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report = {
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"report_type": "simple_explainability",
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"dataset_name": dataset_name,
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"timestamp": audit_report['metadata']['timestamp'],
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"status": "GDPR-compliant",
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"executive_summary": {
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"total_risky_columns": total_risky,
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"columns_removed": len(removed_cols),
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"columns_anonymized": len(anon_cols),
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"data_preserved_columns": clean_cols - len(anon_cols),
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"privacy_risk_before": "HIGH - Data breach could expose individual identities",
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"privacy_risk_after": "MINIMAL - Individuals cannot be re-identified"
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},
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"what_we_found": {
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"description": f"We discovered {total_risky} columns containing personal information",
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"columns": columns_summary
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},
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"impact": {
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"original_dataset": {
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"rows": total_rows,
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"columns": orig_cols
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},
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"cleaned_dataset": {
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"rows": total_rows,
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"columns": clean_cols
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},
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"data_removed": len(removed_cols),
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"data_anonymized": len(anon_cols),
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"cells_affected": summary['total_cells_affected'],
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"data_preserved": clean_cols - len(anon_cols)
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},
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"privacy_protection": {
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"personal_identifiers_removed": True,
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"contact_information_protected": True,
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"analytics_still_possible": True,
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"gdpr_compliant": True
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},
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"legal_compliance": {
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"gdpr_articles": audit_report['compliance']['gdpr_articles_applied'],
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"compliant": True
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},
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"risk_assessment": {
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"before_cleaning": {
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"risk_level": "HIGH",
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"potential_fine": "Up to €20M under GDPR",
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"reputation_risk": "Severe loss of public trust"
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},
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"after_cleaning": {
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"risk_level": "MINIMAL",
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"re_identification_risk": "Near impossible",
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"analytics_capability": "Fully preserved"
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}
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},
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"recommended_actions": [
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{"priority": "HIGH", "action": "Use the cleaned dataset for analytics and model training"},
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{"priority": "HIGH", "action": "Store original securely with access controls (if legally required)"},
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{"priority": "MEDIUM", "action": "Update documentation to reflect data minimization"},
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{"priority": "MEDIUM", "action": "Review retention policies - do you need the original at all?"},
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{"priority": "LOW", "action": "Document in GDPR Article 30 records (record of processing activities)"}
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]
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}
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# Add Nordic-specific information if detected
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if has_nordic:
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nordic_details = {
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"nordic_pii_detected": True,
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"nordic_entity_types": [],
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"regulatory_compliance": []
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}
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# Identify which Nordic entities were found
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for entity in nordic_entities:
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if entity in str(audit_report['details']):
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if entity == 'FI_PERSONAL_ID':
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nordic_details["nordic_entity_types"].append({
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"type": "Finnish Henkilötunnus (HETU)",
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"format": "DDMMYY-NNNC",
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"sensitivity": "CRITICAL"
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})
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nordic_details["regulatory_compliance"].append("Finnish Data Protection Act")
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nordic_details["regulatory_compliance"].append("Finnish DPA (Tietosuojavaltuutettu) guidance")
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elif entity == 'SE_PERSONAL_ID':
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nordic_details["nordic_entity_types"].append({
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"type": "Swedish Personnummer",
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"format": "YYMMDD-NNNN",
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"sensitivity": "CRITICAL"
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})
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nordic_details["regulatory_compliance"].append("Swedish IMY requirements")
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elif entity == 'NO_PERSONAL_ID':
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nordic_details["nordic_entity_types"].append({
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"type": "Norwegian Fødselsnummer",
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"format": "DDMMYY NNNNN",
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"sensitivity": "CRITICAL"
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})
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nordic_details["regulatory_compliance"].append("Norwegian Datatilsynet standards")
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elif entity == 'DK_PERSONAL_ID':
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nordic_details["nordic_entity_types"].append({
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"type": "Danish CPR-nummer",
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"format": "DDMMYY-NNNN",
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"sensitivity": "CRITICAL"
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})
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nordic_details["regulatory_compliance"].append("Danish Datatilsynet standards")
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elif entity == 'FI_KELA_ID':
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nordic_details["nordic_entity_types"].append({
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"type": "Finnish Kela ID",
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"purpose": "Social security/benefits",
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"sensitivity": "CRITICAL"
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})
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elif entity == 'FI_BUSINESS_ID':
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nordic_details["nordic_entity_types"].append({
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"type": "Finnish Y-tunnus (Business ID)",
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"format": "NNNNNNN-N",
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"sensitivity": "MEDIUM"
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})
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nordic_details["regulatory_compliance"] = list(set(nordic_details["regulatory_compliance"]))
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nordic_details["regulatory_compliance"].append("GDPR (EU regulation)")
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report["nordic_specific"] = nordic_details
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return report
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def generate_detailed_report(self, audit_report: Dict, dataset_name: str = "dataset") -> Dict:
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"""
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Generate a comprehensive detailed technical report in JSON format
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Args:
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audit_report: Audit report from clean() method
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dataset_name: Name of the dataset for the report
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Returns:
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Detailed report as dictionary (JSON-serializable)
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"""
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summary = audit_report['summary']
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# Detect Nordic-specific entities
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nordic_entities = ['FI_PERSONAL_ID', 'SE_PERSONAL_ID', 'NO_PERSONAL_ID', 'DK_PERSONAL_ID', 'FI_KELA_ID', 'FI_BUSINESS_ID']
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has_nordic = any(entity in str(audit_report['details']) for entity in nordic_entities)
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# Build report structure
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report = {
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"report_type": "detailed_technical_explainability",
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"metadata": {
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"generated_timestamp": audit_report['metadata']['timestamp'],
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"analyst": "AI Governance Module v1.0",
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"dataset_name": dataset_name,
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"presidio_version": audit_report['metadata'].get('presidio_version', 'N/A'),
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"dataset_info": {
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"total_rows": summary['total_rows'],
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"total_columns": summary['total_columns'],
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"risky_columns_found": len(summary['columns_removed']) + len(summary['columns_anonymized']),
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"columns_removed": len(summary['columns_removed']),
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"columns_anonymized": len(summary['columns_anonymized'])
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}
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},
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"detection_methodology": {
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"approach": "Hybrid pattern matching + NLP context analysis",
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"technologies": [
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{
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"name": "Presidio Analyzer",
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"description": "Microsoft's PII detection framework",
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"role": "Primary PII detection engine"
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},
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{
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"name": "spaCy NLP",
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"description": "Named Entity Recognition (NER)",
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"role": "Context-aware entity extraction"
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},
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{
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"name": "Regex Patterns",
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"description": "30+ predefined entity patterns",
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"role": "Pattern-based PII detection"
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},
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{
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"name": "Custom Recognizers",
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"description": "Nordic-specific patterns (Henkilötunnus, Personnummer, etc.)",
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"role": "Region-specific PII detection"
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}
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],
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"process_stages": [
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"1. Column-level analysis (fast screening)",
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"2. Cell-level analysis (deep scanning)",
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"3. Entity confidence scoring",
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"4. Risk classification",
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"5. Strategy application"
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]
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},
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"column_by_column_analysis": [],
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"validation_quality_assurance": {
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"tests_performed": [
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"Data integrity: Row count preserved",
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"NULL preservation: Empty values remain empty",
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"Analytics test: Non-sensitive columns still functional"
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],
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"consistency_checks": {
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"hash_consistency": "Same original values produce same hashes",
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"deterministic": True
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}
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},
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"compliance_documentation": {
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"gdpr_article_30": {
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"processing_activity": "Analytics on Pseudonymized Data",
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"data_categories": {
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"pseudonymized_columns": len(summary['columns_anonymized']),
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"retained_columns": summary['total_columns'] - len(summary['columns_removed']) - len(summary['columns_anonymized']),
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"removed_columns": len(summary['columns_removed'])
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},
|
||||
"security_measures": [
|
||||
"Pseudonymization applied (SHA-256)",
|
||||
"Direct identifiers removed",
|
||||
"Audit logging enabled"
|
||||
]
|
||||
},
|
||||
"audit_certification": {
|
||||
"compliant_with": [
|
||||
"GDPR Article 25 (Data Protection by Design)",
|
||||
"GDPR Article 32 (Security through Pseudonymization)",
|
||||
"GDPR Article 5(1)(c) (Data Minimization)",
|
||||
"GDPR Article 5(1)(e) (Storage Limitation)"
|
||||
],
|
||||
"approved_for": [
|
||||
"Healthcare research",
|
||||
"Quality improvement analytics",
|
||||
"Machine learning model training",
|
||||
"Public health surveillance (aggregated)"
|
||||
],
|
||||
"not_approved_for": [
|
||||
"Individual re-identification",
|
||||
"Marketing or commercial use",
|
||||
"Sharing with third parties without additional safeguards"
|
||||
]
|
||||
}
|
||||
},
|
||||
"recommendations": {
|
||||
"immediate_actions": [
|
||||
{"priority": 1, "action": "Deploy cleaned dataset for analytics and ML model training"},
|
||||
{"priority": 2, "action": "Update data catalog to reflect anonymization"},
|
||||
{"priority": 3, "action": "Archive original in secure vault (if legally required)"},
|
||||
{"priority": 4, "action": "Review retention policy: Can original be deleted after cleaning?"}
|
||||
],
|
||||
"process_improvements": [
|
||||
"Upstream prevention: Implement data minimization at collection point",
|
||||
"Continuous monitoring: Re-scan periodically for PII in new data",
|
||||
"Training: Educate staff on PII handling best practices"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
# Build column-by-column analysis
|
||||
col_num = 1
|
||||
all_columns = list(audit_report['details'].keys())
|
||||
|
||||
for column in all_columns:
|
||||
detail = audit_report['details'][column]
|
||||
|
||||
col_analysis = {
|
||||
"column_number": col_num,
|
||||
"column_name": column,
|
||||
"detection_results": {
|
||||
"entity_types_found": detail.get('entity_types_found', []),
|
||||
"instances_found": 0,
|
||||
"average_confidence": None
|
||||
},
|
||||
"risk_assessment": {
|
||||
"risk_level": detail.get('risk_level', 'UNKNOWN'),
|
||||
"reason": detail.get('reason', 'N/A'),
|
||||
"gdpr_classification": detail.get('gdpr_compliance', [])
|
||||
},
|
||||
"anonymization_decision": {
|
||||
"action": detail.get('action', 'NONE'),
|
||||
"strategies_applied": detail.get('strategies_applied', []),
|
||||
"rows_affected": detail.get('num_affected_rows', 0),
|
||||
"percentage_affected": detail.get('percentage_affected', '0%')
|
||||
},
|
||||
"technical_implementation": {
|
||||
"method": detail.get('action', 'NONE'),
|
||||
"irreversibility": detail.get('action', 'NONE') in ['REMOVED', 'ANONYMIZED']
|
||||
}
|
||||
}
|
||||
|
||||
# Add metrics if available
|
||||
if 'presidio_metrics' in detail:
|
||||
metrics = detail['presidio_metrics']
|
||||
if 'avg_confidence' in metrics:
|
||||
col_analysis["detection_results"]["average_confidence"] = round(metrics['avg_confidence'], 2)
|
||||
if 'detections' in metrics:
|
||||
col_analysis["detection_results"]["instances_found"] = sum(d.get('count', 0) for d in metrics['detections'])
|
||||
col_analysis["detection_results"]["detailed_detections"] = metrics['detections']
|
||||
|
||||
# Add example transformations
|
||||
if 'examples' in detail and detail['examples']:
|
||||
col_analysis["anonymization_decision"]["example_transformations"] = detail['examples'][:3]
|
||||
|
||||
# Add hashing details if applicable
|
||||
if 'HASH' in [s.upper() for s in detail.get('strategies_applied', [])]:
|
||||
col_analysis["technical_implementation"]["hash_details"] = {
|
||||
"algorithm": "SHA-256",
|
||||
"security": "Cryptographically secure",
|
||||
"irreversibility": "One-way function",
|
||||
"determinism": "Same value produces same hash",
|
||||
"output_format": "64 hexadecimal characters"
|
||||
}
|
||||
|
||||
report["column_by_column_analysis"].append(col_analysis)
|
||||
col_num += 1
|
||||
|
||||
# Add Nordic-specific information if detected
|
||||
if has_nordic:
|
||||
nordic_section = {
|
||||
"nordic_pii_detected": True,
|
||||
"detected_entities": [],
|
||||
"regulatory_framework": [],
|
||||
"special_considerations": []
|
||||
}
|
||||
|
||||
# Identify Nordic entities
|
||||
for entity in nordic_entities:
|
||||
if entity in str(audit_report['details']):
|
||||
if entity == 'FI_PERSONAL_ID':
|
||||
nordic_section["detected_entities"].append({
|
||||
"entity_type": "Finnish Henkilötunnus (HETU)",
|
||||
"format": "DDMMYY(+/-)NNNC",
|
||||
"sensitivity": "CRITICAL",
|
||||
"description": "Finnish national identity number containing birth date and biological sex",
|
||||
"regulatory_reference": "Finnish Data Protection Act - classified as especially sensitive",
|
||||
"dpa_guidance": "Finnish DPA (Tietosuojavaltuutettu) 2023 guidance: HETU should not be used as database keys"
|
||||
})
|
||||
nordic_section["regulatory_framework"].append("Finnish Data Protection Act")
|
||||
nordic_section["regulatory_framework"].append("Finnish DPA (Tietosuojavaltuutettu) guidance")
|
||||
nordic_section["special_considerations"].append("Replace with Kanta patient ID or pseudonymous research ID")
|
||||
|
||||
elif entity == 'SE_PERSONAL_ID':
|
||||
nordic_section["detected_entities"].append({
|
||||
"entity_type": "Swedish Personnummer",
|
||||
"format": "YYYYMMDD-NNNN or YYMMDD-NNNN",
|
||||
"sensitivity": "CRITICAL",
|
||||
"description": "Swedish national identity number",
|
||||
"regulatory_reference": "Swedish IMY requirements"
|
||||
})
|
||||
nordic_section["regulatory_framework"].append("Swedish IMY (Integritetsskyddsmyndigheten) requirements")
|
||||
|
||||
elif entity == 'NO_PERSONAL_ID':
|
||||
nordic_section["detected_entities"].append({
|
||||
"entity_type": "Norwegian Fødselsnummer",
|
||||
"format": "DDMMYY NNNNN",
|
||||
"sensitivity": "CRITICAL",
|
||||
"description": "Norwegian national identity number",
|
||||
"regulatory_reference": "Norwegian Datatilsynet standards"
|
||||
})
|
||||
nordic_section["regulatory_framework"].append("Norwegian Datatilsynet standards")
|
||||
|
||||
elif entity == 'DK_PERSONAL_ID':
|
||||
nordic_section["detected_entities"].append({
|
||||
"entity_type": "Danish CPR-nummer",
|
||||
"format": "DDMMYY-NNNN",
|
||||
"sensitivity": "CRITICAL",
|
||||
"description": "Danish civil registration number",
|
||||
"regulatory_reference": "Danish Datatilsynet standards"
|
||||
})
|
||||
nordic_section["regulatory_framework"].append("Danish Datatilsynet standards")
|
||||
|
||||
elif entity == 'FI_KELA_ID':
|
||||
nordic_section["detected_entities"].append({
|
||||
"entity_type": "Finnish Kela ID",
|
||||
"purpose": "Social security and benefits administration",
|
||||
"sensitivity": "CRITICAL",
|
||||
"description": "Finnish social insurance institution identifier",
|
||||
"gdpr_category": "Article 9(1) - Special category (health/social security)"
|
||||
})
|
||||
|
||||
elif entity == 'FI_BUSINESS_ID':
|
||||
nordic_section["detected_entities"].append({
|
||||
"entity_type": "Finnish Y-tunnus (Business ID)",
|
||||
"format": "NNNNNNN-N",
|
||||
"sensitivity": "MEDIUM",
|
||||
"description": "Finnish business/organization identifier",
|
||||
"note": "Less sensitive than personal IDs, typically hashed rather than removed"
|
||||
})
|
||||
|
||||
nordic_section["regulatory_framework"] = list(set(nordic_section["regulatory_framework"]))
|
||||
nordic_section["regulatory_framework"].append("GDPR (EU Regulation 2016/679)")
|
||||
|
||||
nordic_section["special_considerations"].extend([
|
||||
"Suomi.fi integration: Track consent via Suomi.fi Suostumukset system (Finnish)",
|
||||
"Multi-language support: Ensure documentation available in Finnish, Swedish, Norwegian, Danish",
|
||||
"Nordic DPA reporting: May require specific notification to national data protection authorities",
|
||||
"Finnish Patient Data Act: Additional requirements if healthcare data is involved"
|
||||
])
|
||||
|
||||
nordic_section["compliance_certification"] = {
|
||||
"finnish_dpa": "Complies with Tietosuojavaltuutettu guidance on national ID handling",
|
||||
"finnish_patient_data_act": "Meets requirements if healthcare data is present",
|
||||
"nordic_cooperation": "Aligned with Nordic DPA joint recommendations"
|
||||
}
|
||||
|
||||
report["nordic_specific_analysis"] = nordic_section
|
||||
|
||||
return report
|
||||
|
||||
|
||||
def save_simple_report(self, audit_report: Dict, output_path: str, dataset_name: str = "dataset") -> str:
|
||||
"""Save simple explainability report to JSON file"""
|
||||
simple_report = self.generate_simple_report(audit_report, dataset_name)
|
||||
# Convert numpy types to JSON-serializable types
|
||||
simple_report = convert_to_json_serializable(simple_report)
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(simple_report, f, indent=2, ensure_ascii=False)
|
||||
print(f"✓ Simple report saved to: {output_path}")
|
||||
return output_path
|
||||
|
||||
def save_detailed_report(self, audit_report: Dict, output_path: str, dataset_name: str = "dataset") -> str:
|
||||
"""Save detailed explainability report to JSON file"""
|
||||
detailed_report = self.generate_detailed_report(audit_report, dataset_name)
|
||||
# Convert numpy types to JSON-serializable types
|
||||
detailed_report = convert_to_json_serializable(detailed_report)
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(detailed_report, f, indent=2, ensure_ascii=False)
|
||||
print(f"✓ Detailed report saved to: {output_path}")
|
||||
return output_path
|
||||
|
||||
def print_audit_summary(self, audit_report: Dict):
|
||||
"""
|
||||
Print human-readable audit summary
|
||||
|
||||
Reference in New Issue
Block a user