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A New Algorithm for Protecting Aggregate Business Microdata via a Remote System

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Privacy in Statistical Databases (PSD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9867))

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Abstract

Releasing business microdata is a challenging problem for many statistical agencies. Businesses with distinct continuous characteristics such as extremely high income could easily be identified while these businesses are normally included in surveys representing the population. In order to provide data users with useful statistics while maintaining confidentiality, some statistical agencies have developed online based tools to allow users to specify and request tables created from microdata. These tools only release perturbed cell values generated from automatic output perturbation algorithms in order to protect each underlying observation against various attacks, such as differencing attacks. An example of the perturbation algorithms has been proposed by Thompson et al. (2013). The algorithm focuses largely on reducing disclosure risks without addressing much on data utility. As a result, the algorithm has limitations, including a limited scope of applicable cells and uncontrolled utility loss. In this paper we introduce a new algorithm for generating perturbed cell values. As a comparison, The new algorithm allows more control over utility loss, while it could also achieve better utility-disclosure tradeoffs in many cases, and is conjectured to be applicable to a wider scope of cells.

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Acknowledgements

We sincerely thank Sybille McKeown and all the others in the Australian Bureau of Statistics for providing so many good feedbacks for the paper.

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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

    Yue Ma, Yan-Xia Lin & James Chipperfield

  2. Australian Bureau of Statistics, Canberra, Australia

    James Chipperfield, John Newman & Victoria Leaver

Authors
  1. Yue Ma
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  2. Yan-Xia Lin
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  3. James Chipperfield
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  4. John Newman
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  5. Victoria Leaver
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Corresponding author

Correspondence to Yue Ma .

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Editors and Affiliations

  1. Universitat Rovira i Virgili, Tarragona, Spain

    Josep Domingo-Ferrer

  2. University of Zagreb, Zagreb, Croatia

    Mirjana Pejić-Bach

Additional information

Disclaimer: Views expressed in this paper are those of the author(s) and do not necessarily represent those of the Australian Bureau of Statistics. Where quoted or used, they should be attributed clearly to the author.

Appendix

Appendix

Table 1. Magnitude values that guarantee 15 % disclosure risk given \(\alpha =0.11\) and minimise the average utility loss for different distributions of top contributor values.
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Table 2. Probability expressions for Table 3. \(m_1=y_1w_1\) and \(\lambda _1=\hat{s}_{-1}\beta \).
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Table 3. Disclosure risk of perturbed estimates generated by the new algorithm.
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Fig. 1.
figure 1

Utility-disclosure plots for Simulation 1 with different \(\alpha \) values. The box-plot represents results generated by the Thompson et al. algorithm and the dotted plot represents results generated by the new algorithm.

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Fig. 2.
figure 2

Utility-disclosure plots for Simulation 2 with different \(\alpha \) values.

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Fig. 3.
figure 3

Utility-disclosure plots for Simulation 3 with different \(\alpha \) values.

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Ma, Y., Lin, YX., Chipperfield, J., Newman, J., Leaver, V. (2016). A New Algorithm for Protecting Aggregate Business Microdata via a Remote System. In: Domingo-Ferrer, J., Pejić-Bach, M. (eds) Privacy in Statistical Databases. PSD 2016. Lecture Notes in Computer Science(), vol 9867. Springer, Cham. https://doi.org/10.1007/978-3-319-45381-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-45381-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45380-4

  • Online ISBN: 978-3-319-45381-1

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