Abstract: This repository presents a Conditional Generative Adversary Networks (GANs) on tabular data (and RDF data) combining with Differential Privacy techniques. Our pre-print publication: Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy.

Author: Chang Sun, Institute of Data Science, Maastricht University Start date: Nov-2021 Status: Under development

Note: "Standing on the shoulders of giants". This repository is inspired by the excellent work of CTGAN from Synthetic Data Vault (SDV), Tensorflow Privacy, and RdfPdans. Highly appreciate they shared the ideas and implementations, made code publicly available, well-written documentation. More related work can be found in the References below.

This package is extended from SDV (https://github.com/sdv-dev/SDV), CTGAN (https://github.com/sdv-dev/CTGAN), and Differential Privacy in GANs (https://github.com/civisanalytics/dpwgan). The author modified the conditional matrix and cost functions to emphasize on the relations between variables. The main changes are in ctgan/synthesizers/ctgan.py ../data_sampler.py ../data_transformer.py

You will need Python >=3.8+ and <=3.11. sdv ==1.6.0, and rdt==1.9.0

pip install dp-cgans

You can easily generate synthetic data for a file using your terminal after installing dp-cgans with pip.

To quickly run our example, you can download the example data:

wget https://raw.githubusercontent.com/sunchang0124/dp_cgans/main/resources/example_tabular_data_UCIAdult.csv

Then run dp-cgans:

dp-cgans gen example_tabular_data_UCIAdult.csv --epochs 2 --output out.csv --gen-size 100

Get a full rundown of the available options for generating synthetic data with:

dp-cgans gen --help

This library can also be used directly in python scripts

If your input is tabular data (e.g., csv):

from dp_cgans import DP_CGAN
import pandas as pd

tabular_data=pd.read_csv("../resources/example_tabular_data_UCIAdult.csv")

# We adjusted the original CTGAN model from SDV. Instead of looking at the distribution of individual variable, we extended to two variables and keep their corrll
model = DP_CGAN(
   epochs=100, # number of training epochs
   batch_size=1000, # the size of each batch
   log_frequency=True,
   verbose=True,
   generator_dim=(128, 128, 128),
   discriminator_dim=(128, 128, 128),
   generator_lr=2e-4, 
   discriminator_lr=2e-4,
   discriminator_steps=1, 
   private=False,
)

print("Start training model")
model.fit(tabular_data)
model.save("generator.pkl")

# Generate 100 synthetic rows
syn_data = model.sample(100)
syn_data.to_csv("syn_data_file.csv")

For development, we recommend to install and use Hatch, as it will automatically install and sync the dependencies when running development scripts. But you can also directly create a virtual environment and install the library with pip install -e .

Clone the repository:

git clone https://github.com/sunchang0124/dp_cgans
cd dp_cgans

When working in development the hatch tool will automatically install and sync the dependencies when running a script. But you can also directly

Run the library with the CLI:

hatch -v run dp-cgans gen --help

You can also enter a new shell with the virtual environments automatically activated:

hatch shell
dp-cgans gen --help

Run the tests locally:

hatch run pytest -s

Run formatting and linting (black and ruff):

hatch run fmt

In case the virtual environments is not updating as expected you can easily reset it with:

hatch env prune

The deployment of new releases is done automatically by a GitHub Action workflow when a new release is created on GitHub. To release a new version:

1. Make sure the PYPI_API_TOKEN secret has been defined in the GitHub repository (in Settings > Secrets > Actions). You can get an API token from PyPI here.

2. Increment the version number in src/dp_cgans/__init__.py file:

   hatch version fix    # Bump from 0.0.1 to 0.0.2
   hatch version minor  # Bump from 0.0.1 to 0.1.0
   hatch version 0.1.1  # Bump to the specified version

3. Create a new release on GitHub, which will automatically trigger the publish workflow, and publish the new release to PyPI.

You can also manually build and publish from you laptop:

hatch build
hatch publish

There are many excellent work on generating synthetic data using GANS and other methods. We list the studies that made great conbributions for the field and inspiring for our work.

1. Synthetic Data Vault (SDV) [Paper] [Github]
2. Modeling Tabular Data using Conditional GAN (a part of SDV) [Paper] [Github]
3. Wasserstein GAN [Paper]
4. Improved Training of Wasserstein GANs [Paper]
5. Synthesising Tabular Data using Wasserstein Conditional GANs with Gradient Penalty (WCGAN-GP) [Paper]
6. PacGAN: The power of two samples in generative adversarial networks [Paper]
7. CTAB-GAN: Effective Table Data Synthesizing [Paper]
8. Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting [Paper]
9. TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks [Paper]
10. Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning [Paper]

1. Tensorflow Privacy [Github]
2. Renyi Differential Privacy [Paper]
3. DP-CGAN : Differentially Private Synthetic Data and Label Generation [Paper]
4. Differentially Private Generative Adversarial Network [Paper] [Github] Another implementation [Github]
5. Private Data Generation Toolbox [Github]
6. autodp: Automating differential privacy computation [Github]
7. Differentially Private Synthetic Medical Data Generation using Convolutional GANs [Paper]
8. DTGAN: Differential Private Training for Tabular GANs [Paper]
9. DIFFERENTIALLY PRIVATE SYNTHETIC DATA: APPLIED EVALUATIONS AND ENHANCEMENTS [Paper]
10. FFPDG: FAST, FAIR AND PRIVATE DATA GENERATION [Paper]

1. EvoGen: a Generator for Synthetic Versioned RDF [Paper]
2. Generation and evaluation of synthetic patient data [Paper]
3. Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation [Paper]
4. Generating and evaluating cross-sectional synthetic electronic healthcare data: Preserving data utility and patient privacy [Paper]
5. Synthetic data for open and reproducible methodological research in social sciences and official statistics [Paper]
6. A Study of the Impact of Synthetic Data Generation Techniques on Data Utility using the 1991 UK Samples of Anonymised Records [Paper]