  Openly sharing data with sensitive attributes and privacy restrictions is a
challenging task. In this document we present the implementation of pyCANON, a
Python library and command line interface (CLI) to check and assess the level
of anonymity of a dataset through some of the most common anonymization
techniques: k-anonymity, ($\alpha$,k)-anonymity, $\ell$-diversity, entropy
$\ell$-diversity, recursive (c,$\ell$)-diversity, basic $\beta$-likeness,
enhanced $\beta$-likeness, t-closeness and $\delta$-disclosure privacy. For the
case of more than one sensitive attributes, two approaches are proposed for
evaluating this techniques. The main strength of this library is to obtain a
full report of the parameters that are fulfilled for each of the techniques
mentioned above, with the unique requirement of the set of quasi-identifiers
and that of sensitive attributes. We present the methods implemented together
with the attacks they prevent, the description of the library, use examples of
the different functions, as well as the impact and the possible applications
that can be developed. Finally, some possible aspects to be incorporated in
future updates are proposed.
