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

Project description

made-with-python License Documentation Status

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

: int

Entropy ℓ-diversity

entropy_l_diversity

: int

Recursive (c,ℓ)-diversity

recursive_c_l_diversity

c: int : 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

Project details


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