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ai_data_privacy_manager [2025/05/25 18:32] – [Required Libraries] eagleeyenebulaai_data_privacy_manager [2025/05/25 18:37] (current) – [Best Practices] eagleeyenebula
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 ==== Basic Examples ==== ==== Basic Examples ====
-Anonymizing sensitive fields and logging records:+**Anonymizing sensitive fields and logging records:**
  
-```python+<code> 
 +python
 from ai_data_privacy_manager import DataPrivacyManager from ai_data_privacy_manager import DataPrivacyManager
- +</code> 
-# Initialize the privacy manager with fields to anonymize+# **Initialize the privacy manager with fields to anonymize** 
 +<code>
 data_privacy_manager = DataPrivacyManager(anonymization_fields=["email", "phone_number"]) data_privacy_manager = DataPrivacyManager(anonymization_fields=["email", "phone_number"])
- +</code> 
-# Input dataset+# **Input dataset** 
 +<code>
 user_data = { user_data = {
     "name": "Alice",     "name": "Alice",
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     "phone_number": "1234567890"     "phone_number": "1234567890"
 } }
- +</code> 
-# Log anonymized data+# **Log anonymized data** 
 +<code>
 data_privacy_manager.log_with_compliance(user_data) data_privacy_manager.log_with_compliance(user_data)
-```+</code>
  
 **Example Log Output:** **Example Log Output:**
-```plaintext+<code> 
 +plaintext
 INFO:root:Compliant log: {'name': 'Alice', 'email': 'cd192d68db7f5b0a6...', 'phone_number': 'fa246d0262c...'} INFO:root:Compliant log: {'name': 'Alice', 'email': 'cd192d68db7f5b0a6...', 'phone_number': 'fa246d0262c...'}
-```+</code>
  
 ---- ----
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 Extend the **DataPrivacyManager** class to use a different hashing mechanism, such as MD5 or SHA-512. Extend the **DataPrivacyManager** class to use a different hashing mechanism, such as MD5 or SHA-512.
  
-```python+<code> 
 +python
 class CustomHashPrivacyManager(DataPrivacyManager): class CustomHashPrivacyManager(DataPrivacyManager):
     def anonymize(self, record):     def anonymize(self, record):
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                 anonymized_record[key] = value                 anonymized_record[key] = value
         return anonymized_record         return anonymized_record
- +</code> 
-# Usage Example+# **Usage Example** 
 +<code>
 custom_manager = CustomHashPrivacyManager(anonymization_fields=["email"]) custom_manager = CustomHashPrivacyManager(anonymization_fields=["email"])
 print(custom_manager.anonymize({"email": "user@example.com"})) print(custom_manager.anonymize({"email": "user@example.com"}))
-```+</code>
  
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 {'email': 'b58996c504c5638798eb6b511e6f49af'} {'email': 'b58996c504c5638798eb6b511e6f49af'}
-```+</code>
  
 --- ---
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 Anonymize fields conditionally, for example, only anonymize emails matching certain domains. Anonymize fields conditionally, for example, only anonymize emails matching certain domains.
  
-```python+<code> 
 +python
 class ConditionalPrivacyManager(DataPrivacyManager): class ConditionalPrivacyManager(DataPrivacyManager):
     def anonymize(self, record):     def anonymize(self, record):
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                 anonymized_record[key] = value                 anonymized_record[key] = value
         return anonymized_record         return anonymized_record
- +</code> 
-# Usage Example+# **Usage Example** 
 +<code>
 conditional_manager = ConditionalPrivacyManager(anonymization_fields=["email"]) conditional_manager = ConditionalPrivacyManager(anonymization_fields=["email"])
 print(conditional_manager.anonymize({"email": "test@example.com", "name": "Bob"})) print(conditional_manager.anonymize({"email": "test@example.com", "name": "Bob"}))
-```+</code>
  
 --- ---
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 Integrate **DataPrivacyManager** into an ETL data pipeline to anonymize sensitive rows before transformation. Integrate **DataPrivacyManager** into an ETL data pipeline to anonymize sensitive rows before transformation.
  
-```python+<code> 
 +python
 class ETLPipeline: class ETLPipeline:
     def __init__(self, privacy_manager):     def __init__(self, privacy_manager):
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         anonymized_data = [self.privacy_manager.anonymize(record) for record in data]         anonymized_data = [self.privacy_manager.anonymize(record) for record in data]
         return anonymized_data         return anonymized_data
- +</code> 
-# Initialize Privacy Manager+# **Initialize Privacy Manager** 
 +<code>
 privacy_manager = DataPrivacyManager(anonymization_fields=["email", "phone_number"]) privacy_manager = DataPrivacyManager(anonymization_fields=["email", "phone_number"])
- +</code> 
-# Pipeline Example+# **Pipeline Example** 
 +<code>
 pipeline = ETLPipeline(privacy_manager=privacy_manager) pipeline = ETLPipeline(privacy_manager=privacy_manager)
 data = [ data = [
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 anonymized_data = pipeline.process(data) anonymized_data = pipeline.process(data)
 print(anonymized_data) print(anonymized_data)
-```+</code>
  
 **Output:** **Output:**
-```plaintext+<code> 
 +plaintext
 [ [
     {'name': 'Alice', 'email': '...', 'phone_number': '...'},     {'name': 'Alice', 'email': '...', 'phone_number': '...'},
     {'name': 'Bob', 'email': '...', 'phone_number': '...'}     {'name': 'Bob', 'email': '...', 'phone_number': '...'}
 ] ]
-```+</code>
  
 ---- ----
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 2. **Test Field Coverage:** 2. **Test Field Coverage:**
-   - Ensure all sensitive fields are listed in `anonymization_fields`.+   - Ensure all sensitive fields are listed in **anonymization_fields**.
  
 3. **Secure Logs:** 3. **Secure Logs:**
ai_data_privacy_manager.1748197947.txt.gz · Last modified: 2025/05/25 18:32 by eagleeyenebula