anjana.anonymity.utils package

Submodules

anjana.anonymity.utils.utils module

Module with utils for the anonymization tools.

anjana.anonymity.utils.utils.apply_hierarchy(data: list | ndarray, hierarchies: dict, level: int) → list | ndarray

Apply the given level of a hierarchy for a quasi-identifier.

Parameters:

• data (list, numpy array) – data under study.

• hierarchies (dictionary with the hierarchies and the levels) – hierarchies for generalizing a given QI.

• level (int) – level of the hierarchy to be applied.

Returns:

column with the given level of hierarchy applied.

Return type:

numpy array

anjana.anonymity.utils.utils.apply_hierarchy_current(data: list | ndarray, hierarchies: dict, level: int, actual: int) → list | ndarray

Apply certain level of a hierarchy for a quasi-identifier given the current one.

Parameters:

• data (list, numpy array) – data under study.

• hierarchies (dictionary with the hierarchies and the levels) – hierarchies for generalizing a given QI.

• level (int) – level of the hierarchy to be applied.

• actual (int) – current level of the hierarchy applied.

Returns:

column with the given level of hierarchy applied.

Return type:

numpy array

anjana.anonymity.utils.utils.apply_transformation(data: DataFrame, quasi_ident: list | ndarray, hierarchies: dict, transformation: list) → DataFrame

Apply a given transformation to the data.

Parameters:

• data (pandas dataframe) – data under study.

• quasi_ident (list of strings) – list with the name of the columns of the dataframe that are quasi-identifiers.

• hierarchies (dictionary containing one dictionary for QI with the hierarchies and the levels) – hierarchies for generalizing the QI.

• transformation (list) – transformation to be applied

Returns:

dataset generalized with the transformation given

Return type:

pandas dataframe

anjana.anonymity.utils.utils.check_gen_level(data: DataFrame, quasi_ident: list | ndarray, hierarchies: dict) → dict

Check the generalization level for each quasi-identifier.

Parameters:

• data (pandas dataframe) – data under study.

• quasi_ident (list of strings) – list with the name of the columns of the dataframe that are quasi-identifiers.

• hierarchies (dictionary containing one dictionary for QI with the hierarchies and the levels) – hierarchies for generalizing the QI.

Returns:

level of generalization applied to each QI.

Return type:

dict

anjana.anonymity.utils.utils.generate_intervals(quasi_ident: list | ndarray, inf: int | float, sup: int | float, step: int) → list

Generate intervals as hierarchies.

Given a quasi-identifier of numeric type, creates a list containing an interval-based generalization (hierarchy) of the values of the quasi-identifier. The intervals will have the length entered in step.

Parameters:

• quasi_ident (list or numpy array) – values of the quasi-identifier on which the interval-based generalization is to be obtained

• inf (int or float) – lower value of the set of intervals

• sup (int or float) – bigger value of the set of intervals

• step (int) – spacing between values of the intervals

Returns:

list with the intervals associated with the given values

Return type:

list

anjana.anonymity.utils.utils.get_transformation(data_anon: DataFrame, quasi_ident: list | ndarray, hierarchies: dict) → list

Get the transformation applied for anonymizing the data.

Example: a transformation [0,1,2,0] means: - Level 0 of generalization for th 1st QI - Level 1 of generalization for th 2nd QI - Level 2 of generalization for th 3rd QI - Level 0 of generalization for the 4th QI

Parameters:

• data_anon (pandas dataframe) – data under study.

• quasi_ident (list of strings) – list with the name of the columns of the dataframe that are quasi-identifiers.

• hierarchies (dictionary containing one dictionary for QI with the hierarchies and the levels) – hierarchies for generalizing the QI.

Returns:

transformation applied

Return type:

list

anjana.anonymity.utils.utils.suppress_identifiers(data: DataFrame, ident: list | ndarray) → DataFrame

Remove the identifiers from a dataset.

Parameters:

• data (pandas dataframe) – data under study.

• ident (list of strings) – list with the name of the columns of the dataframe that are identifiers.

Returns:

data with the identifiers suppressed.

Return type:

pandas dataframe

Module contents

Package containing auxiliary functions for performing the anonymization.

anjana.anonymity.utils.apply_hierarchy(data: list | ndarray, hierarchies: dict, level: int) → list | ndarray

Apply the given level of a hierarchy for a quasi-identifier.

Parameters:

• data (list, numpy array) – data under study.

• hierarchies (dictionary with the hierarchies and the levels) – hierarchies for generalizing a given QI.

• level (int) – level of the hierarchy to be applied.

Returns:

column with the given level of hierarchy applied.

Return type:

numpy array

anjana.anonymity.utils.apply_transformation(data: DataFrame, quasi_ident: list | ndarray, hierarchies: dict, transformation: list) → DataFrame

Apply a given transformation to the data.

Parameters:

• data (pandas dataframe) – data under study.

• quasi_ident (list of strings) – list with the name of the columns of the dataframe that are quasi-identifiers.

• hierarchies (dictionary containing one dictionary for QI with the hierarchies and the levels) – hierarchies for generalizing the QI.

• transformation (list) – transformation to be applied

Returns:

dataset generalized with the transformation given

Return type:

pandas dataframe

anjana.anonymity.utils.check_gen_level(data: DataFrame, quasi_ident: list | ndarray, hierarchies: dict) → dict

Check the generalization level for each quasi-identifier.

Parameters:

• data (pandas dataframe) – data under study.

• quasi_ident (list of strings) – list with the name of the columns of the dataframe that are quasi-identifiers.

• hierarchies (dictionary containing one dictionary for QI with the hierarchies and the levels) – hierarchies for generalizing the QI.

Returns:

level of generalization applied to each QI.

Return type:

dict

anjana.anonymity.utils.generate_intervals(quasi_ident: list | ndarray, inf: int | float, sup: int | float, step: int) → list

Generate intervals as hierarchies.

Given a quasi-identifier of numeric type, creates a list containing an interval-based generalization (hierarchy) of the values of the quasi-identifier. The intervals will have the length entered in step.

Parameters:

• quasi_ident (list or numpy array) – values of the quasi-identifier on which the interval-based generalization is to be obtained

• inf (int or float) – lower value of the set of intervals

• sup (int or float) – bigger value of the set of intervals

• step (int) – spacing between values of the intervals

Returns:

list with the intervals associated with the given values

Return type:

list

anjana.anonymity.utils.get_transformation(data_anon: DataFrame, quasi_ident: list | ndarray, hierarchies: dict) → list

Get the transformation applied for anonymizing the data.

Example: a transformation [0,1,2,0] means: - Level 0 of generalization for th 1st QI - Level 1 of generalization for th 2nd QI - Level 2 of generalization for th 3rd QI - Level 0 of generalization for the 4th QI

Parameters:

• data_anon (pandas dataframe) – data under study.

• quasi_ident (list of strings) – list with the name of the columns of the dataframe that are quasi-identifiers.

• hierarchies (dictionary containing one dictionary for QI with the hierarchies and the levels) – hierarchies for generalizing the QI.

Returns:

transformation applied

Return type:

list

anjana.anonymity.utils.suppress_identifiers(data: DataFrame, ident: list | ndarray) → DataFrame

Remove the identifiers from a dataset.

Parameters:

• data (pandas dataframe) – data under study.

• ident (list of strings) – list with the name of the columns of the dataframe that are identifiers.

Returns:

data with the identifiers suppressed.

Return type:

pandas dataframe