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Protecting Values Close to Zero Under the Multiplicative Noise Method

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Book cover Privacy in Statistical Databases (PSD 2018)

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

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Abstract

Perturbing sensitive data is one of the standard ways of protecting confidential data. The multiplicative noise method is one of these data perturbation methods and this method has attracted researchers’ attention in the recent decade. However, values close to zero in datasets cannot be well protected by using the multiplicative noise method directly. This paper proposes a method for safeguarding the values close to zero through noise-multiplied shifted data. We demonstrate that those values can be reasonably protected through noise-multiplied data by following the approach proposed in this paper. This paper also indicates that the density function of the original data can be reasonably reconstructed from the noise-multiplied shifted data by using the software MaskDensity14 or MaskDensityBM.

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Notes

  1. 1.

    Software MaskDensityBM is available on request.

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Authors and Affiliations

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

    Yan-Xia Lin

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  1. Yan-Xia Lin
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Correspondence to Yan-Xia Lin .

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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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Lin, YX. (2018). Protecting Values Close to Zero Under the Multiplicative Noise Method. 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_17

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  • DOI: https://doi.org/10.1007/978-3-319-99771-1_17

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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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