See also the accompanying WhiteNoise-System and WhiteNoise-Core repositories for this system.
Differential privacy is the gold standard definition of privacy protection. The WhiteNoise project aims to connect theoretical solutions from the academic community with the practical lessons learned from real-world deployments, to make differential privacy broadly accessible to future deployments. Specifically, we provide several basic building blocks that can be used by people involved with sensitive data, with implementations based on vetted and mature differential privacy research. In WhiteNoise Samples we provide example code and notebooks to:
- demonstrate the use of the WhiteNoise platform,
- teach the properties of differential privacy,
- highlight some of the nuances of the WhiteNoise implementation.
Notebooks on Library Usage: A set of notebooks showing how to create differentially private releases using the WhiteNoise library and private analysis validator. The library and validator are both written in Rust, but the notebooks are Python and demonstrate the use of our Python bindings.
Notebooks on SQL Data Access: A set of notebooks showing how to use SQL to create differentially private reports.
Notebooks on Stochastic Evaluation: Notebooks demonstrating the use of the stochastic evaluator.
WhiteNoise Core Library Reference: The Core Library implments the runtime validator and execution engine.
WhiteNoise System SDK Reference:. The System SDK implements the SQL Data Access, Execution Service, and Stochastic Evaluator.