https://github.com/alan-turing-institute/tapas

https://github.com/alan-turing-institute/tapas

hut23 hut23-1013

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• Host: GitHub
• URL: https://github.com/alan-turing-institute/tapas
• Owner: alan-turing-institute
• License: mit
• Created: 2022-03-24T13:39:13.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-09-06T15:45:06.000Z (about 1 year ago)
• Last Synced: 2024-05-21T01:11:15.528Z (6 months ago)
• Topics: hut23, hut23-1013
• Language: Python
• Homepage:
• Size: 3.79 MB
• Stars: 30
• Watchers: 15
• Forks: 14
• Open Issues: 42
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-trustworthy-deep-learning - TAPAS - turing-institute/privacy-sdg-toolbox) (Privacy Toolboxes)

README

        
![tests](https://github.com/alan-turing-institute/privacy-sdg-toolbox/actions/workflows/ci.yml/badge.svg) [![Documentation Status](https://readthedocs.org/projects/tapas-privacy/badge/?version=latest)](https://tapas-privacy.readthedocs.io/en/latest/index.html)

# 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](https://arxiv.org/abs/2211.06550).

[Documentation.](https://tapas-privacy.readthedocs.io/en/latest/index.html)

## 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](https://python-poetry.org/docs/).

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.)