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Efficiency and Sample Size Determination of Protected Data

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Privacy in Statistical Databases (PSD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11126))

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Abstract

This paper assesses the usefulness of a proposed multiplicative perturbation method by contrasting the statistical efficiency achieved in point hypothesis testing of simple proportions with that of the differentially private aggregated Laplace mechanism. This efficiency is evaluated by obtaining an analytical expression that determines the sample size required for protected data to retain a given significance level and power.

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Notes

  1. 1.

    When \(N_p\) is larger, there is no significant difference between \(1/(N_p-1)\) and \(1/N_p\). To simplify the calculation, we use \(1/N_p\) instead of \(1/(N_p-1)\).

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Acknowledgement

This research has been conducted with the support of the Australian Government Research Training Program Scholarship.

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Authors and Affiliations

  1. National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia

    Bradley Wakefield & Yan-Xia Lin

Authors
  1. Bradley Wakefield
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  2. Yan-Xia Lin
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Correspondence to Bradley Wakefield .

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Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Josep Domingo-Ferrer

  2. University of Valencia, Burjassot, Spain

    Francisco Montes

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Wakefield, B., Lin, YX. (2018). Efficiency and Sample Size Determination of Protected Data. In: Domingo-Ferrer, J., Montes, F. (eds) Privacy in Statistical Databases. PSD 2018. Lecture Notes in Computer Science(), vol 11126. Springer, Cham. https://doi.org/10.1007/978-3-319-99771-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-99771-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99770-4

  • Online ISBN: 978-3-319-99771-1

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