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).
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]
The pyCANON documentation is hosted on Read the Docs.
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)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.
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}}
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.