pyCANON: A Python library to check the level of anonymity of a dataset#

pyCANON is a Python library which allows the user to know the anonymity level of a dataset based on a set of quasi-identifiers (QI), and a set of sensitive attributes. To do so, it provides a set of functions to compute the anonymity level of a dataset by means of the following techniques:

  • k-anonymity.

  • (\(\alpha\),k)-anonymity.

  • \(\ell\)-diversity.

  • Entropy \(\ell\)-diversity.

  • Recursive (c, \(\ell\))-diversity.

  • t-closeness.

  • Basic \(\beta\)-likeness.

  • Enhanced \(\beta\)-likeness.

  • \(\delta\)-disclosure privacy.

User documentation#

Getting started#

Using pyCANON is quite straighforward.

Install#

We recommend using pip for installing pyCANON inside a virtualenv:

virtualenv .venv
source .venv/bin/activate
pip install pycanon

Installing with support for PDF reports: If you want to generate PDF reports with ReportLab you need to issut the following install command:

pip install pycanon[PDF]

If you want to use the latest development version, you can use:

virtualenv .venv
source .venv/bin/activate
git clone https://gitlab.ifca.es/privacy-security/pycanon/
pip install pycanon

Example usage#

You can use pyCANON through the command line or via its Python API.

Command line#

Example with the adult dataset:

$ pycanon k-anonymity --qi age --qi education --qi occupation --qi relationship --qi sex --qi native-country adult.csv

$ pycanon report --sa salary-class --qi age --qi education --qi occupation --qi relationship --qi sex --qi native-country adult.csv
Python API#

Example with the adult dataset:

from pycanon import anonymity, report

FILE_NAME = "adult.csv"
QI = ["age", "education", "occupation", "relationship", "sex", "native-country"]
SA = ["salary-class"]
DATA = pd.read_csv(FILE_NAME)

# Calculate k for k-anonymity:
k = anonymity.k_anonymity(DATA, QI)

# Print the anonymity report:
report.print_report(DATA, QI, SA)

pycanon#

pycanon package#

Subpackages#
pycanon.anonymity package#
Subpackages#
pycanon.anonymity.utils package#
Submodules#
pycanon.anonymity.utils.aux_anonymity module#

Module with different functions which calculate properties about anonymity.

k-anonymity, (alpha,k)-anonymity, l-diversity, entropy l-diversity, (c,l)-diversity, basic beta-likeness, enhanced beta-likeness, t-closeness and delta-disclosure privacy.

pycanon.anonymity.utils.aux_anonymity.aux_calculate_beta(data: DataFrame, quasi_ident: Union[list, ndarray], sens_att_value: str) → Tuple[ndarray, list]#

Beta calculation for basic and enhanced beta-likeness.

Parameters:

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

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

  • sens_att_value (string) – sensitive attribute under study.

Returns:

proportion of each value of the sensitive attribute in the entire database and distance from the proportion in each equivalence class.

Return type:

np.array and list.

pycanon.anonymity.utils.aux_anonymity.aux_calculate_delta_disclosure(data: DataFrame, quasi_ident: Union[list, ndarray], sens_att_value: str) → float#

Delta calculation for delta-disclosure privacy.

Parameters:

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

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

  • sens_att_value (string) – sensitive attribute under study.

Returns:

delta for the introduced SA.

Return type:

float.

pycanon.anonymity.utils.aux_anonymity.aux_t_closeness_num(data: DataFrame, quasi_ident: Union[list, ndarray], sens_att_value: str) → float#

Obtain t for t-closeness.

Function used for numerical attributes: the definition of the EMD is used.

Parameters:

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

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

  • sens_att_value (string) – sensitive attribute under study.

Returns:

t for the introduced SA (numerical).

Return type:

float.

pycanon.anonymity.utils.aux_anonymity.aux_t_closeness_str(data: DataFrame, quasi_ident: Union[list, ndarray], sens_att_value: list) → float#

Obtain t for for t-closeness.

Function used for categorical attributes: the metric “Equal Distance” is used.

Parameters:

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

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

  • sens_att_value (string) – sensitive attribute under study.

Returns:

t for the introduced SA (categorical).

Return type:

float.

pycanon.anonymity.utils.aux_anonymity.get_equiv_class(data: DataFrame, quasi_ident: Union[list, ndarray]) → list#

Find the equivalence classes present in the dataset.

Parameters:

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

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

Returns:

equivalence classes.

Return type:

list.

pycanon.anonymity.utils.aux_functions module#

Module with different auxiliary functions.

pycanon.anonymity.utils.aux_functions.check_qi(data: DataFrame, quasi_ident: Union[List, ndarray]) → None#

Check if the entered quasi-identifiers are valid.

Parameters:

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

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

pycanon.anonymity.utils.aux_functions.check_sa(data: DataFrame, sens_att: Union[List, ndarray]) → None#

Check if the entered sensitive attributes are valid.

Parameters:

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

  • sens_att (is a list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

pycanon.anonymity.utils.aux_functions.convert(ec_set: set) → list#

Convert a set with an equivalence class to a list.

Parameters:

ec_set (set) – set which will be convert into a list.

Returns:

equivalence class into a list.

Return type:

list.

pycanon.anonymity.utils.aux_functions.intersect(tmp: list) → list#

Intersect two sets: the first and the second of the given list.

Parameters:

tmp (list of numpy arrays) – list of sets sorted in decreasing order of cardinality

Returns:

list obtained when intersecting the first and the second sets of the given list.

Return type:

list.

pycanon.anonymity.utils.aux_functions.read_file(file_name: Union[str, Path], sep: str = ',') → DataFrame#

Read the given file. Returns a pandas dataframe.

Parameters:

  • file_name (string or pathlib.Path) – file with the data under study.

  • sep (string) – delimiter to use for a csv file.

Returns:

dataframe with the data.

Return type:

pandas dataframe.

Module contents#

Package containing auxiliary functions related with privacy models.

Module contents#

Module with different functions which calculate properties about anonymity.

k-anonymity, (alpha,k)-anonymity, l-diversity, entropy l-diversity, (c,l)-diversity, basic beta-likeness, enhanced beta-likeness, t-closeness and delta-disclosure privacy.

pycanon.anonymity.alpha_k_anonymity(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → Tuple[float, int]#

Calculate alpha and k for (alpha,k)-anonymity.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

alpha and k values for (alpha,k)-anonymity.

Return type:

alpha is a float, k is an int.

pycanon.anonymity.basic_beta_likeness(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → float#

Calculate beta for basic beta-likeness.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

beta value for basic beta-likeness.

Return type:

float.

pycanon.anonymity.delta_disclosure(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → float#

Calculate delta for delta-disclousure privacy.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

delta value for delta-discloure privacy.

Return type:

float.

pycanon.anonymity.enhanced_beta_likeness(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → float#

Calculate beta for enhanced beta-likeness.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

beta value for enhanced beta-likeness.

Return type:

float.

pycanon.anonymity.entropy_l_diversity(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → float#

Calculate l for entropy l-diversity.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

l value for entropy l-diversity.

Return type:

float.

pycanon.anonymity.k_anonymity(data: DataFrame, quasi_ident: Union[List, ndarray]) → int#

Calculate k for k-anonymity.

Parameters:

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

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

Returns:

k value for k-anonymity.

Return type:

int.

pycanon.anonymity.l_diversity(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → int#

Calculate l for l-diversity.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

l value for l-diversity.

Return type:

int.

pycanon.anonymity.recursive_c_l_diversity(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], imp=False, gen=True) → Tuple[float, int]#

Calculate c and l for recursive (c,l)-diversity.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

c and l values for recursive (c,l)-diversity.

Return type:

c is a float, l is an int.

pycanon.anonymity.t_closeness(data: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray], gen=True) → float#

Calculate t for t-closeness.

Parameters:

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

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – boolean, default to True. If true, it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA

Returns:

t value for basic t-closeness.

Return type:

float.

pycanon.report package#
Submodules#
pycanon.report.base module#

Get report values for all privacy models.

pycanon.report.base.get_report_values(data: DataFrame, quasi_ident: list, sens_att: list, gen=True) → Tuple[int, Tuple[float, int], int, float, Tuple[Any, int], float, float, float, float]#

Generate a report with the parameters obtained for each anonymity check.

Parameters:

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

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

  • sens_att (is a list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – default to true. If true it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA.

pycanon.report.json module#

Get report values as JSON for all privacy models.

pycanon.report.json.get_json_report(data: DataFrame, quasi_ident: list, sens_att: list, gen=True) → str#

Generate a report (JSON) with the parameters obtained for each anonymity check.

Parameters:

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

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

  • sens_att (is a list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – default to true. If true it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA.

pycanon.report.pdf module#
pycanon.report.pdf_utility_report module#
Module contents#

Generate reports with all privacy model’s scores.

pycanon.report.get_json_report(data: DataFrame, quasi_ident: list, sens_att: list, gen=True) → str#

Generate a report (JSON) with the parameters obtained for each anonymity check.

Parameters:

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

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

  • sens_att (is a list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – default to true. If true it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA.

pycanon.report.get_report_values(data: DataFrame, quasi_ident: list, sens_att: list, gen=True) → Tuple[int, Tuple[float, int], int, float, Tuple[Any, int], float, float, float, float]#

Generate a report with the parameters obtained for each anonymity check.

Parameters:

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

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

  • sens_att (is a list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – default to true. If true it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA.

pycanon.report.print_report(data: DataFrame, quasi_ident: list, sens_att: list, gen=True) → None#

Generate a report with the parameters obtained for each anonymity check.

Parameters:

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

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

  • sens_att (is a list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

  • gen (boolean) – default to true. If true it is generalized for the case of multiple SA, if False, the set of QI is updated for each SA.

pycanon.utility package#
Module contents#

Module with different functions for calculating the utility.

pycanon.utility.average_ecsize(data_raw: DataFrame, data_anon: DataFrame, quasi_ident: Union[List, ndarray], sup=True) → float#

Calculate the metric average equivalence class size.

Parameters:

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

  • data_anon (pandas dataframe) – dataframe with the data anonymized.

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

  • sup (boolean) – boolean, default to True. If true, suppression has been applied to the original dataset (some records may have been deleted).

pycanon.utility.classification_metric(data_raw: DataFrame, data_anon: DataFrame, quasi_ident: Union[List, ndarray], sens_att: Union[List, ndarray]) → float#

Calculate the classification metric.

Parameters:

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

  • data_anon (pandas dataframe) – dataframe with the data anonymized.

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

  • sens_att (list of strings) – list with the name of the columns of the dataframe that are the sensitive attributes.

pycanon.utility.discernability_metric(data_raw: DataFrame, data_anon: DataFrame, quasi_ident: Union[List, ndarray]) → float#

Calculate the discernability metric.

Parameters:

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

  • data_anon – dataframe with the data anonymized. Assuming that all the

equivalence classes have more than k records, and given each suppressed record a penalty of the size of the input dataset. :type data_anon: pandas dataframe

Parameters:

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

pycanon.utility.sizes_ec(data: DataFrame, quasi_ident: Union[List, ndarray]) → dict#

Calculate statistics associated to the equivalence classes.

Parameters:

  • data (pandas dataframe) – dataframe with the data anonymized.

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

pycanon.utility.stats_quasi_ident(data: DataFrame, quasi_ident: str) → dict#

Calculate statistics associated to a given quasi-identifier.

Parameters:

  • data (pandas dataframe) – dataframe with the data anonymized.

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

Submodules#
pycanon.cli module#

Module providing command line tools for pyCANON.

pycanon.cli.alpha_k_anonymity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate (alpha,k)-anonymity.

pycanon.cli.basic_beta_likeness(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate basic beta-likeness.

pycanon.cli.delta_disclosure(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate delta-disclosure.

pycanon.cli.enhanced_beta_likeness(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate enhanced beta-likeness.

pycanon.cli.entropy_l_diversity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate entropy l-diversity.

pycanon.cli.k_anonymity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>)#

Calculate k-anonymity.

pycanon.cli.l_diversity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate l-diversity.

pycanon.cli.main(version: ~typing.Optional[bool] = <typer.models.OptionInfo object>)#

Check the level of anonymity of a dataset.

pycanon.cli.recursive_c_l_diversity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate recursive (c,l)-diversity.

pycanon.cli.report(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Generate a complete privacy report.

pycanon.cli.t_closeness(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)#

Calculate t-closeness.

pycanon.cli.version_callback(version: bool)#

Return version info.

Module contents#

pyCANON is a library to check the values of the most common data privacy models.

Notes and Warnings#

Note

In the case of multiple sensitve attributes, two approaches can be considered:

  • Calculate the different properties for each sensitive attribute individually, and take the maximum or minimum as appropriate (for example, the minumum value of \(\ell\) for \(\ell\)-diversity and the maximum value for \(\alpha\) in the case of (\(\alpha\),k)-anonymity).

  • Calculate the different properties for each sensitive attribute individually but modifying the set of quasi-identifiers by adding to this set, in addition to the initial quasi-identifiers, all the sensitive attributes except the one under analysis. Then, as in the previous approach, the minimum or maximum of each parameter as appropriate is taken. It is important to note that since the set of quasi-identifiers is updated each time the calculations are made for each SA, the computational cost is much higher in this case.

Specifically, in order to address this challenge, a parameter gen is introduced in all functions except k-anonymity (not applicable). If gen=True (default value), the process of the first approach is followed: generalizing. Otherwise, the second approach is followed, updating the quasi-identifiers.

Warning

It’s important to take into account that the values of t and \(\delta\) for t-closeness and \(\delta\)-disclosure privacy respectively must be strictly greater than the ones obtained using pyCANON (see the definition of that techniques).

Utility metrics#

The details of the three utility metrics implemented are presented below:

  • Average equivalence class size (\(C_{avg}\)): Two versions are implemented depending on whether or not complete rows have been deleted from the original dataset (in this case, sup=True in get_utility_report_values). Be DB the original database and DB’ the anonymized one. Be |EC| the number of equivalence classes in the anonymized dataset and k the verified value for k-anonymity:

\[\begin{split}C_{avg} = \left\{ \begin{array}{ll} \frac{|DB'|}{k \hspace{0.1cm} |EC|} & \hspace{0.3cm} \mbox{if } sup=True\\ \frac{|DB|}{k \hspace{0.1cm} |EC|} & \mbox{if } sup=False\\ \end{array} \right.\end{split}\]

  • Classification metric (CM): This metric is calculted for only one sensitive attribute. Be N the number of rows of the raw datatset. Be \(penalty(r_{i})=1\) if the row \(r_{i}\) has been suppresed or if its associated sensitive attribute takes a value other than the majority in the equivalence class to which it belongs, and 0 otherwise. The classification metric is defined as folows:

\[CM = \frac{1}{N}\sum_{i=1}^{N}penalty(r_{i}),\]

  • Discernability metric (\(DM^{*}\)): A version of the classical discernability metric has been considered in order to penalize the deletion of records. Thus, it has been implemented according to the following equation, with DB the raw database, DB’ the anonymized one, \(N_{ec}\) the number of equivalence classes and \(EC_{i}\) the i-th equivalence class:

\[DM^{*} = \sum_{i=0}^{N_{ec}}|EC_{i}|^{2} + |DB|^{2}-|DB||DB'|\]

Note: for the calculation of these three metrics, it is necessary to enter the original dataset prior to anonymization.

License#

pyCANON is licensed under Apache License Version 2.0 (http://www.apache.org/licenses/)

Indices and tables#