Data Anonymization and Pseudonymization Tools

Data Anonymization and Pseudonymization Tools

Proper anonymization requires more than just removing names. Tools for data anonymization help implement k-anonymity, l-diversity, and t-closeness. These tools analyze datasets for re-identification risks and apply appropriate transformations to protect individual privacy while maintaining data utility.

ARX is a comprehensive data anonymization tool providing a GUI and API for implementing various privacy models. It includes risk analysis, data quality metrics, and transformation suggestions. For Python users, libraries like anonymizedf and cape-privacy provide programmatic anonymization capabilities.

# Using cape-privacy for data anonymization
from cape_privacy import pandas as cape_pd
import pandas as pd

class DataAnonymizer:
    def __init__(self):
        self.policy = self.create_anonymization_policy()
    
    def create_anonymization_policy(self):
        """Define anonymization rules"""
        return cape_pd.policy.Policy(
            label="user_data_anonymization",
            rules=[
                # Generalize age to ranges
                cape_pd.GeneralizationRule(
                    column="age",
                    bins=[0, 18, 25, 35, 45, 55, 65, 100],
                    labels=["<18", "18-24", "25-34", "35-44", "45-54", "55-64", "65+"]
                ),
                
                # Round coordinates to reduce precision
                cape_pd.NumericPerturbationRule(
                    column="latitude",
                    min_=-0.01,
                    max_=0.01
                ),
                cape_pd.NumericPerturbationRule(
                    column="longitude", 
                    min_=-0.01,
                    max_=0.01
                ),
                
                # Hash email addresses
                cape_pd.ReversibleTokenizationRule(
                    column="email",
                    key="secret_key_for_tokenization"
                ),
                
                # Redact sensitive fields
                cape_pd.RedactionRule(
                    column="ssn"
                ),
                
                # Generalize timestamps
                cape_pd.DateTruncationRule(
                    column="created_at",
                    frequency="week"
                ),
                
                # K-anonymity for quasi-identifiers
                cape_pd.KAnonymityRule(
                    quasi_identifiers=["age_range", "zipcode", "gender"],
                    k=5
                )
            ]
        )
    
    def anonymize_dataset(self, df):
        """Apply anonymization policy to dataset"""
        # Apply policy
        anonymized_df = self.policy.apply(df)
        
        # Verify k-anonymity
        if not self.verify_k_anonymity(anonymized_df, k=5):
            # Apply additional generalization if needed
            anonymized_df = self.increase_generalization(anonymized_df)
        
        return anonymized_df
    
    def verify_k_anonymity(self, df, k=5):
        """Check if dataset satisfies k-anonymity"""
        quasi_identifiers = ["age_range", "zipcode", "gender"]
        groups = df.groupby(quasi_identifiers).size()
        return all(count >= k for count in groups)