Privacy evaluation framework for synthetic data publishing

Implementation of a privacy evaluation framework for synthetic data publishing

Attack models

The module attack_models so far includes

A privacy adversary to test for privacy gain with respect to linkage attacks modelled as a membership inference attack MIAAttackClassifier.

A simple attribute inference attack AttributeInferenceAttack that aims to infer a target's sensitive value given partial knowledge about the target record

Generative models

The module generative_models so far includes:

• IndependentHistogramModel: An independent histogram model adapted from Data Responsibly's DataSynthesiser
• BayesianNetModel: A generative model based on a Bayesian Network adapted from Data Responsibly's DataSynthesiser
• GaussianMixtureModel: A simple Gaussian Mixture model taken from the sklearn library
• CTGAN: A conditional tabular generative adversarial network that integrates the CTGAN model from CTGAN
• PATE-GAN: A differentially private generative adversarial network adapted from its original implementation

Setup

Requirements

The framework and its building blocks have been developed and tested under Python 3.6 and 3.7

We recommend to create a virtual environment for installing all dependencies and running the code

python3 -m venv pyvenv3
source pyvenv3/bin/activate
pip install -r requirements.txt

Dependencies

The CTGAN model depends on a fork of the original model training algorithm that can be found here CTGAN-SPRING

To install the correct version clone the repository above and run

cd CTGAN
make install

To test your installation try to run

import ctgan

from within your virtualenv python

Example runs

To run a privacy evaluation with respect to the privacy concern of linkability you can run

python linkage_cli.py -D data/texas -RC tests/linkage/runconfig.json -O tests/linkage

The results file produced after successfully running the script can be parsed with the function load_results_mia provided in utils/analyse_results.py.

To run a privacy evaluation with respect to the privacy concern of inference you can run

python inference_cli.py -D data/texas -RC tests/inference/runconfig.json -O tests/inference

The results file produced after successfully running the script can be parsed with the function load_results_ai provided in utils/analyse_results.py.