DP Wizard guides the user through the application of differential privacy. After selecting a local CSV, users are prompted to describe to the anlysis they need. Output options include:
- A Jupyter notebook which demonstrates how to use OpenDP.
- A plain Python script.
- Text and CSV reports.
usage: dp-wizard [-h] [--public_csv CSV] [--private_csv CSV] [--contrib CONTRIB] [--demo]
DP Wizard makes it easier to get started with Differential Privacy.
options:
-h, --help show this help message and exit
--public_csv CSV Path to public CSV
--private_csv CSV Path to private CSV
--contrib CONTRIB How many rows can an individual contribute?
--demo Use generated fake CSV for a quick demo
Use "--public_csv" if you have a public data set, and are curious how
DP can be applied: The preview visualizations will use your public data.
Use "--private_csv" if you only have a private data set, and want to
make a release from it: The preview visualizations will only use
simulated data, and apart from the headers, the private CSV is not
read until the release.
Use "--public_csv" and "--private_csv" together if you have two CSVs
with the same structure. Perhaps the public CSV is older and no longer
sensitive. Preview visualizations will be made with the public data,
but the release will be made with private data.