Awesome
Privacy evaluation framework for synthetic data publishing
A practical framework to evaluate the privacy-utility tradeoff of synthetic data publishing
Based on "Synthetic Data - Anonymisation Groundhog Day, Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso, arXiv, 2020"
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:
IndependentHistogram: An independent histogram model adapted from Data Responsibly's DataSynthesiserBayesianNet: A generative model based on a Bayesian Network adapted from Data Responsibly's DataSynthesiserPrivBayes: A differentially private version of the BayesianNet model adapted from Data Responsibly's DataSynthesiserCTGAN: A conditional tabular generative adversarial network that integrates the CTGAN model from CTGANPATE-GAN: A differentially private generative adversarial network adapted from its original implementation by the MLforHealth Lab
Setup
Docker Distribution
For your convenience, Synthetic Data is also distributed as a ready-to-use Docker image containing Python 3.9 and CUDA 11.4.2, along with all dependencies required by Synthetic Data, including jupyter notebook to visualise and analyse the results.
Note: This distribution includes CUDA binaries, before downloading the image, ensure to read its EULA and to agree to its terms.
Pull the image and run a container (and bind a volume where you want to save the data):
docker pull springepfl/synthetic-data:latest
docker run -it --rm -v "$(pwd)/output:/output" -p 8888:8888 springepfl/synthetic-data
The Synthetic Data directory is placed at the root directory of the container.
cd /synthetic_data_release
You should now be able to run the examples without encountering any problems, and you should be able to visualize the results with Jupyter by running
jupyter notebook --allow-root --ip=0.0.0.0
and opening the notebook with your favourite web browser at the url http://127.0.0.1:8888/?token=<authentication token>.
Direct Installation
Requirements
The framework and its building blocks have been developed and tested under Python 3.9 .
We recommend to create a virtual environment for installing all dependencies and running the code
python3 -m venv pyvenv3
source pyvenv3/bin/activate
pip install numpy==1.19.5 && pip install -r requirements.txt
Note: Some people encountered problems due to the API of Numpy having changed between versions, to ensure all dependencies are compiled against the same Numpy version, it needs to be installed first.
Dependencies
The CTGAN model depends on a fork of the original model training algorithm that can be found here
CTGAN-SPRING
To install the correct version clone the repository above and run
cd CTGAN
make install
Add the path to this directory to your python path. You can also add this line
in your shell configuration file (e.g., ~/.bashrc) to load it automatically.
# Execute this in the CTGAN folder, otherwise replace `pwd` with the actual path
export PYTHONPATH=$PYTHONPATH:`pwd`
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
python3 linkage_cli.py -D data/texas -RC tests/linkage/runconfig.json -O tests/linkage
The results file produced after successfully running the script will be written to tests/linkage and can be parsed with the function load_results_linkage provided in utils/analyse_results.py.
A jupyter notebook to visualise and analyse the results is included at notebooks/Analyse Results.ipynb.
To run a privacy evaluation with respect to the privacy concern of inference you can run
python3 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_inference provided in utils/analyse_results.py.
A jupyter notebook to visualise and analyse the results is included at notebooks/Analyse Results.ipynb.
To run a utility evaluation with respect to a simple classification task as utility function run
python3 utility_cli.py -D data/texas -RC tests/utility/runconfig.json -O tests/utility
The results file produced after successfully running the script can be parsed with the function load_results_utility provided in utils/analyse_results.py.