  We present d3p, a software package designed to help fielding runtime
efficient widely-applicable Bayesian inference under differential privacy
guarantees. d3p achieves general applicability to a wide range of probabilistic
modelling problems by implementing the differentially private variational
inference algorithm, allowing users to fit any parametric probabilistic model
with a differentiable density function. d3p adopts the probabilistic
programming paradigm as a powerful way for the user to flexibly define such
models. We demonstrate the use of our software on a hierarchical logistic
regression example, showing the expressiveness of the modelling approach as
well as the ease of running the parameter inference. We also perform an
empirical evaluation of the runtime of the private inference on a complex model
and find a $\sim$10 fold speed-up compared to an implementation using
TensorFlow Privacy.
