trasgodp.categorical package

Module contents

DP Exponential mechanism for categorical columns.

trasgodp.categorical.dp_exponential(df: DataFrame, column: str, epsilon: float, new_column=False) → DataFrame

Apply the Exponential mechanism to a categorical column of a dataframe.

Parameters:

  • df (pandas dataframe) – dataframe with the data under study.

  • columm (string) – column to which the DP mechanism will be applied.

  • epsilon (float) – privacy budget.

  • new_column (boolean) – boolean, default to False. If False, the new values obtained with the mechanims applied are stored in the same column. If True, a new column ‘dp_{column}’ is created with the new values.

Returns:

dataframe with the column transformed applying the mechanism.

Return type:

pandas dataframe.

trasgodp.categorical.dp_exponential_array(data: List | ndarray, epsilon: float) → ndarray

Apply the Exponential mechanism to an array with categorical values.

Parameters:

  • data (list or numpy array) – dataset with the data under study.

  • epsilon (float) – privacy budget.

Returns:

array with data transformed applying the mechanism.

Return type:

numpy array.

trasgodp.categorical.dp_randomized_response_binary(df: DataFrame, column: str, epsilon: float, new_column=False, positive_label=None) → DataFrame

Apply the Randomized Response mechanism to a binary column of a dataframe.

Parameters:

  • df (pandas dataframe) – dataframe with the data under study.

  • columm (string) – column to which the DP mechanism will be applied. Binary data.

  • epsilon (float) – privacy budget.

  • new_column (boolean) – boolean, default to False. If False, the new values obtained with the mechanims applied are stored in the same column. If True, a new column ‘dp_{column}’ is created with the new values.

  • positive_label (string) – value to be assigned as 1. If None, it is assigned to first value.

Returns:

dataframe with the column transformed applying the mechanism.

Return type:

pandas dataframe.

trasgodp.categorical.dp_randomized_response_binary_array(data: list | ndarray, epsilon: float, positive_label=None) → ndarray

Apply the binary randomized response mechanism to a list or numpy array.

Parameters:

  • data (list or numpy array) – dataset with the data under study.

  • epsilon (float) – privacy budget.

  • positive_label (string) – value to be assigned as 1. If None, it is assigned to first value.

Returns:

array with the data transformed applying the binary RR mechanism.

Return type:

numpy array.

trasgodp.categorical.dp_randomized_response_kary(df: DataFrame, column: str, epsilon: float, new_column=False) → DataFrame

Apply the Randomized Response mechanism to column of a dataframe (no binary).

Parameters:

  • df (pandas dataframe) – dataframe with the data under study.

  • columm (string) – column to which the DP mechanism will be applied. Binary data.

  • epsilon (float) – privacy budget.

  • new_column (boolean) – boolean, default to False. If False, the new values obtained with the mechanims applied are stored in the same column. If True, a new column ‘dp_{column}’ is created with the new values.

Returns:

dataframe with the column transformed applying the mechanism.

Return type:

pandas dataframe.

trasgodp.categorical.dp_randomized_response_kary_array(data: list | ndarray, epsilon: float) → ndarray

Apply the k-ary Randomized Response mechanism to a list or numpy array.

Parameters:

  • data (list or numpy array) – dataset with the data under study.

  • epsilon (float) – privacy budget.

  • positive_label (string) – value to be assigned as 1. If None, it is assigned to first value.

Returns:

array with the data transformed applying the k-ary RR mechanism.

Return type:

numpy array.