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privacy preserving

PRIVACY_PRESERVING

Source code in engines/contentFilterEngine/fairness_explainability/privacy_preserving.py 
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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

__init__()

Initialize the privacy-preserving module.

Source code in engines/contentFilterEngine/fairness_explainability/privacy_preserving.py 
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def __init__(self):
    """
    Initialize the privacy-preserving module.
    """
    self.anonymized_data = {}

anonymize_data(user_data)

Anonymize user data to preserve privacy.

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

Returns: - pd.DataFrame: Anonymized user data.

Source code in engines/contentFilterEngine/fairness_explainability/privacy_preserving.py 
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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

apply_differential_privacy(data, epsilon)

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.

Source code in engines/contentFilterEngine/fairness_explainability/privacy_preserving.py 
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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