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Empirical Evaluation of Statistical Inference from Differentially-Private Contingency Tables

  • Anne-Sophie Charest
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7556)

Abstract

In this paper, we evaluate empirically the quality of statistical inference from differentially-private synthetic contingency tables. We compare three methods: histogram perturbation, the Dirichlet-Multinomial synthesizer and the Hardt-Ligett-McSherry algorithm. We consider a goodness-of-fit test for models suitable to the real data, and a model selection procedure. We find that the theoretical guarantees associated with these differentially-private datasets do not always translate well into guarantees about the statistical inference on the synthetic datasets.

Keywords

Contingency Table Synthetic Dataset Model Selection Procedure Multiplicative Weight Synthetic Data Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anne-Sophie Charest
    • 1
  1. 1.Université LavalCanada

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