Ceilyn Boyd (Oct 07 2025 at 16:57): Ceilyn Boyd (Oct 07 2025 at 16:57):Hi everyone,
I know some of you are interested in working with and sharing sensitive data using differential privacy (DP), or have researchers in your user communities who are interested in doing so. But learning DP concepts can be intimidating.
I'd like to bring to your attention a new tool developed by the Harvard OpenDP group, called DP Wizard. DP Wizard makes it easier to get started with differential privacy. It demonstrates how to calculate DP statistics or create a synthetic dataset from the data you provide.
The DP Wizard team would especially welcome feedback from researchers in your communities who are new to differential privacy concepts but are interested in how the tech might help them.
The software is open source, allowing you to run DP Wizard locally and upload your own CSV file, or use the cloud deployment option and only provide column names to protect your private data.
You'll be prompted to describe your privacy budget and the analysis you need. With that information, DP Wizard provides:
A Jupyter notebook that demonstrates how to use the OpenDP Library.
A plain Python script.
Text and CSV reports.
Please try it out and share your feedback using the in-app tool or by creating GitHub issues and pull requests at the DP Wizard GitHub repository.
We don't currently have an integration between DP Wizard and Dataverse, but it could be something interesting to explore.
@Ellen K and @Cheng Shi have worked on the project, so they may be able to answer implementation-specific questions or have other thoughts about DP Wizard they'd like to share.
Thanks, all!
Last updated: Nov 01 2025 at 14:11 UTC