privkit 0.2.0

Privkit is a privacy toolkit that provides methods for privacy analysis. It implements different data types, privacy-preserving mechanisms, attacks, and privacy/utility metrics.

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Privkit: A Privacy Toolkit

Privkit is a privacy toolkit that provides methods for privacy analysis. It includes different data types, privacy-preserving mechanisms, attacks, and metrics. The current version is focused on location data and facial data. The Python Package is designed in a modular manner and can be easily extended to include new mechanisms. Privkit can be used to process data, configure privacy-preserving mechanisms, apply attacks, and also evaluate the privacy/utility trade-off through suitable metrics.

See https://privkit.fc.up.pt for a complete documentation.

Citation

If you use privkit in your work, please consider to cite:

@inproceedings{cunha2024privkit,
  title={Privkit: A Toolkit of Privacy-Preserving Mechanisms for Heterogeneous Data Types},
  author={Cunha, Mariana and Duarte, Guilherme and Andrade, Ricardo and Mendes, Ricardo and Vilela, Jo{\~a}o P},
  booktitle={Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy},
  pages={319--324},
  year={2024}
}

Installation

Privkit can be installed through this Github repository or by using pip:

pip install privkit

Then, if needed, you can run the following command to install the dependencies.

pip install -r requirements.txt

Creating a virtual environment

Note that you can create a virtual environment (pipenv), which is optional but strongly recommended, in order to avoid potential conflicts with other packages. You just need to download the Pipfile and Pipfile.lock files and run in the same directory:

pipenv install

Then you can access the virtual environment by executing:

pipenv shell

Examples

The repository https://github.com/privkit/privkit-tutorials contains practical tutorials of Privkit available as Jupyter Notebooks. This repository aims at promoting the reproducibility of results from research papers.

Getting started

import privkit as pk

data_to_load = [['2008-10-23 02:53:04', 39.984702, 116.318417],
                ['2008-10-23 02:53:10', 39.984683, 116.31845],
                ['2008-10-23 02:53:15', 39.984686, 116.318417]]

location_data = pk.LocationData()
location_data.load_data(data_to_load, datetime=0, latitude=1, longitude=2)

planar_laplace = pk.ppms.PlanarLaplace(epsilon=0.01)
planar_laplace.execute(location_data)

License

Privkit is open source and licensed under the BSD 3-clause license.

Acknowledgments

We would like to acknowledge the support of PRIVATEER.