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Empirical Evaluation of Statistical Inference from Differentially-Private Contingency Tables

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Privacy in Statistical Databases (PSD 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7556))

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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.

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Author information

Authors and Affiliations

  1. Université Laval, Canada

    Anne-Sophie Charest

Authors
  1. Anne-Sophie Charest
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Editor information

Editors and Affiliations

  1. Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

  2. Dipartimento di Ingegneria Elettrica, Elettronica e delle Telecomunicazioni, di tecnologie Chimiche, Automatica e modelli Matematici, Università degli Studi di Palermo, Viale delle Scienze, edificio 9, 90128, Palermo, Italy

    Ilenia Tinnirello

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Charest, AS. (2012). Empirical Evaluation of Statistical Inference from Differentially-Private Contingency Tables. In: Domingo-Ferrer, J., Tinnirello, I. (eds) Privacy in Statistical Databases. PSD 2012. Lecture Notes in Computer Science, vol 7556. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33627-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-33627-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33626-3

  • Online ISBN: 978-3-642-33627-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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