D3p -- A Python Package for Differentially-Private Probabilistic Programming

TL;DR

d3p is a Python package enabling efficient Bayesian inference under differential privacy for a wide range of probabilistic models, leveraging probabilistic programming for flexibility and demonstrating significant runtime improvements.

Contribution

The paper introduces d3p, a novel software package that implements differentially private variational inference within a probabilistic programming framework, enhancing applicability and efficiency.

Findings

Successfully applied to hierarchical logistic regression

Achieved approximately 10-fold speed-up over TensorFlow Privacy

Demonstrated flexibility in defining complex probabilistic models

Abstract

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.