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07 February 2025 |
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Article overview
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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 | Abstract: | We 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 | Services: | Forum | Review | PDF | Favorites |
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