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07 February 2025
 
  » arxiv » 1605.0283

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Differentially Private Bayesian Programming
Gilles Barthe ; Gian Pietro Farina ; Marco Gaboardi ; Emilio Jesùs Gallego Arias ; Andy Gordon ; Justin Hsu ; Pierre-Yves Strub ;
Date 1 May 2016
AbstractWe present an expressive framework, called PrivInfer, for writing and verifying differentially private machine learning algorithms. Programs in PrivInfer are written in a rich functional probabilistic language with constructs for performing Bayesian inference. Then, differential privacy of programs is established using a relational refinement type system, in which refinements on probability types are indexed by a metric on distributions. Our framework leverages recent developments in Bayesian inference, probabilistic program- ming languages, and in relational refinement types. We demonstrate the expressiveness of PrivInfer by verifying privacy for several examples of private Bayesian inference.
Source arXiv, 1605.0283
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