pycanon 1.0.1.post2

Description:

pycanon 1.0.1.post2

pyCANON is a Python library and CLI to assess the values of the parameters
associated with the most common privacy-preserving techniques via anonymization.
Authors: Judith Sáinz-Pardo Díaz and Álvaro López García (IFCA - CSIC).

Installation
We recommend to use Python3 with
virtualenv:
virtualenv .venv -p python3
source .venv/bin/activate
Then run the following command to install the library and all its
requirements:
pip install pycanon
If you also want to install the functionality that allows to generate PDF files
for the reports, install as follows
pip install pycanon[PDF]


Documentation
The pyCANON documentation is hosted on Read the
Docs.


Getting started
Example using the adult
dataset:
import pandas as pd
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)


Description
pyCANON allows to check if the following privacy-preserving techniques
are verified and the value of the parameters associated with each of
them.


Technique
pyCANON function
Parameters
Notes



k-anonymity
k_anonymity
k: int


(α, k)-anonymity
alpha_k_anonymity
α: float
k:int


ℓ-diversity
l_diversity
ℓ: int


Entropy ℓ-diversity
entropy_l_diversity
ℓ: int


Recursive (c,ℓ)-diversity
recursive_c_l_diversity
c: int
ℓ: int
Not calculated if ℓ=1

Basic β-likeness
basic_beta_likeness
β: float


Enhanced β-likeness
enhanced_beta_likeness
β: float


t-closeness
t_closeness
t: float
For numerical attributes the definition of the EMD
(one-dimensional Earth Mover’s Distance) is used.
For categorical attributes, the metric “Equal
Distance” is used.

δ-disclosure privacy
delta_disclosure
δ: float




More information can be found in this paper.
In addition, a report can be obtained including information on the equivalence claases and the
usefulness of the data. In particular, for the latter the following three classically used metrics
are implemented (as defined in the documentation):
average equivalence class size, classification metric and discernability metric.


Citation
If you are using pyCANON you can cite it as follows:
@article{sainzpardo2022pycanon,
title={A Python library to check the level of anonymity of a dataset},
author={S{\'a}inz-Pardo D{\'\i}az, Judith and L{\'o}pez Garc{\'\i}a, {\'A}lvaro},
journal={Scientific Data},
volume={9},
number={1},
pages={785},
year={2022},
publisher={Nature Publishing Group UK London}}


Acknowledgments
The authors would like to thank the funding through the European Union - NextGenerationEU
(Regulation EU 2020/2094), through CSIC’s Global Health Platform (PTI+ Salud Global) and
the support from the project AI4EOSC “Artificial Intelligence for the European Open Science
Cloud” that has received funding from the European Union’s Horizon Europe research and
innovation programme under grant agreement number 101058593.