d3p -- A Python Package for Differentially-Private Probabilistic Programming

Probabilistic modelling presents a natural way to model data by describing their (assumed) generative process. The model is then fit to observations by probabilistic inference algorithms. Learning from sensitive data, however, clearly raises concerns about privacy, calling for privacy-preserving model inference algorithms. Differential privacy (DP) [10] provides a rigorous mathematical framework for addressing such concerns and has become the de-facto standard notion for privacy. It essentially assures that an algorithms outputs will not differ significantly whether a specific individual's data record is included in the data set or not.