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Differentially Private Bayesian Programming

Published • Oct 24, 2016
NobleIDNI0P16W52R01S79
Authors:
Gilles Barthe
,
Gian Pietro Farina
,
Marco Gaboardi

Abstract

We present PrivInfer, an expressive framework for writing and verifying differentially private Bayesian machine learning algorithms. Programs in PrivInfer are written in a rich functional probabilistic programming language with constructs for performing Bayesian inference. Then, differential privacy...

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