pycanon 1.0.0

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 | | +—————————-+————————–+———————-+——————————-+