For developers and data scientists, we recommend viewing the SmartNoise Samples repository which provides example code and notebooks to:
- Demonstrate the use of the system platform
- Teach the properties of differential privacy
- Highlight some of the nuances of the system implementation
Figure 1. History of Education.
Source: OpenDP Development Team, “Histograms,” SmartNoise Samples: Differential Privacy Examples, Notebooks, and Documentation, 2020. https://github.com/opendp/smartnoise-samples/blob/master/analysis/histograms.ipynb (accessed Mar. 04, 2021).
Based on public California census data, this example histogram above shows true values compared to differentially private, or DP, values. The DP values give high accuracy while protecting individual privacy. For details, please see the histogram notebook.
These examples use the SmartNoise Core Python bindings and SmartNoise SDK which provide basic building blocks for working with sensitive data, with implementations based on vetted and mature differential privacy research.
- Please contact us on Gitter or send an email to firstname.lastname@example.org..
- For bugs, we encourage you to use GitHub issues.