TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

Evaluating the privacy of synthetic data with an adversarial toolbox. This code implements the TAPAS toolbox presented in the associated paper.

Documentation.

Reference

If you use this toolbox for a scientific publication, we kindly ask you to reference the paper:

Houssiau, F., Jordon, J., Cohen, S.N., Daniel, O., Elliott, A., Geddes, J., Mole, C., Rangel-Smith, C. and Szpruch, L., 2022. _TAPAS: a toolbox for adversarial privacy auditing of synthetic data._

In BibTex:

@article{houssiau2022tapas,
  title={TAPAS: a toolbox for adversarial privacy auditing of synthetic data},
  author={Houssiau, F and Jordon, J and Cohen, SN and Daniel, O and Elliott, A and Geddes, J and Mole, C and Rangel-Smith, C and Szpruch, L},
  year={2022},
  publisher={Neural Information Processing Systems Foundation}
}

Direct Installation

Requirements

The framework and its building blocks have been developed and tested under Python 3.9.

Poetry installation

To mimic our environment exactly, we recommend using poetry. To install poetry (system-wide), follow the instructions here.

Then run

poetry install

from inside the project directory. This will create a virtual environment (default .venv), that can be accessed by running poetry shell, or in the usual way (with source .venv/bin/activate).

Pip installation (includes command-line tool)

It is also possible to install from pip:

pip install git+https://github.com/alan-turing-institute/privacy-sdg-toolbox

Doing so installs a command-line tool, tapas, somewhere in your path. (Eg, on a MacOS system with pip installed via homebrew, the tool ends up in a homebrew bin director.)