Welcome! In this repository we are creating an overview of useful privacy engineering tools, i.e., tools to make it easier to handle personal data in research. For example, we are collecting useful packages/tools to help deidentify personal data, but also to create synthetic micro-data.

This repository is part of the Data Privacy Project, an RDM Support project at Utrecht University that aims to improve information, tools and services surrounding personal data in research.

Current overviews can be accessed here:

• Mostly Python- and some R-based packages to generate synthetic & simulated data
• Python-, R-based and user interface tools to deidentify data
• Software to securely analyse data, e.g., to bring code-to-data or encrypt data.

Important notes:

1. In no way do we claim that these overviews are complete or fully accurate. Please let us know if we are missing important tools and/or if an existing entry needs correction.
2. We are not responsible for the quality of the tools in this overview. Moreover, the quality of the results when using these tools is largely dependent on your own use of the tool. We recommend to use these tools responsibly and be wary of unrealistic promises that tools may make. If a tool we included turns out to be of low quality, please let us know so we can take note of that.

This repository is licensed under an MIT license. You can view the license text here.

For questions about this repository, please contact Utrecht University's Research Data Management Support, or open an Issue or Pull request in this repository.