Privattacks: A Python package for evaluating privacy risks in tabular datasets

Privattacks is a Python package for evaluating privacy risks in tabular datasets. It provides tools to quantify re-identification and attribute inference vulnerabilities based on combinations of quasi-identifiers an adversary knows about a target. The package supports both high-level and granular analysis, including per-record vulnerability distributions, multiprocessing for efficiency, and flexible input handling via pandas DataFrames and other sources.

Repository & Documentation

The source code is available at Github and the documentation here.

Available tools

• Prior vulnerability for re-identification and attribute inference.
• Posterior vulnerability for a given combination of qids and/or sensitive attribute.
• Posterior vulnerability for a subset of all possible combinations of QIDs.
• Parellel code.
• Generate the histogram of vulnerabilities (i.e., vulnerability per record).

Installation

You can install via PyPI:

pip install privattacks

or manuallly by copying this repository to your local machine and running:

pip install path/to/privattacks

To verify if the package was install corretly, you can run tests:

cd path/to/privattacks
python -m unittest discover tests

License

This project is licensed under the MIT License – see the LICENSE file for details.