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d3p - A Python Package for Differentially-Private Probabilistic Programming

d3p - A Python Package for Differentially-Private Probabilistic Programming AbstractWe 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 ~10 fold speed-up compared to an implementation using TensorFlow Privacy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

d3p - A Python Package for Differentially-Private Probabilistic Programming

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References (56)

Publisher
de Gruyter
Copyright
© 2022 Lukas Prediger et al., published by Sciendo
ISSN
2299-0984
eISSN
2299-0984
DOI
10.2478/popets-2022-0052
Publisher site
See Article on Publisher Site

Abstract

AbstractWe 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 ~10 fold speed-up compared to an implementation using TensorFlow Privacy.

Journal

Proceedings on Privacy Enhancing Technologiesde Gruyter

Published: Apr 1, 2022

Keywords: differential privacy; JAX; NumPyro; probabilistic programming; variational inference

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