Differentially Private Bayesian Programming

@inproceedings{Barthe2016DifferentiallyPB,
  title={Differentially Private Bayesian Programming},
  author={Gilles Barthe and Gian Pietro Farina and Marco Gaboardi and Emilio Jes{\'u}s Gallego Arias and Andrew D. Gordon and Justin Hsu and Pierre-Yves Strub},
  booktitle={ACM Conference on Computer and Communications Security},
  year={2016}
}
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 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… CONTINUE READING
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