pycanon 1.0.0.post4

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

pyCANON is a library and CLI to assess the values of the paramenters associated with the most common privacy-preserving techniques.

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

Documentation

The pyCANON documentation is hosted on Read the Docs.

Getting started

Example using the adult dataset:

from pycanon import anonymity, report

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

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

# Print the anonymity report:
report.print_report(FILE_NAME, 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