# privacy_preserving implementation
import pandas as pd

class PRIVACY_PRESERVING:
    def __init__(self):
        """
        Initialize the privacy-preserving module.
        """
        self.anonymized_data = {}

    def anonymize_data(self, user_data: pd.DataFrame) -> pd.DataFrame:
        """
        Anonymize user data to preserve privacy.

        Parameters:
        - user_data (pd.DataFrame): DataFrame containing user information.

        Returns:
        - pd.DataFrame: Anonymized user data.
        """
        # Example: Remove identifiable information
        anonymized_data = user_data.drop(columns=['user_id', 'zip_code'])
        self.anonymized_data = anonymized_data
        return anonymized_data

    def apply_differential_privacy(self, data: pd.DataFrame, epsilon: float) -> pd.DataFrame:
        """
        Apply differential privacy to the data.

        Parameters:
        - data (pd.DataFrame): DataFrame containing data to be privatized.
        - epsilon (float): Privacy budget parameter.

        Returns:
        - pd.DataFrame: Data with differential privacy applied.
        """
        # Placeholder: Implement differential privacy logic
        return data
