privkit 0.1

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 a scientific publication, 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

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.