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Reviewing the Methods of Estimating the Density Function Based on Masked 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

Data privacy is an issue of increasing importance for big data mining, especially for micro-level data. A popular approach to protecting the such is perturbation. Therefore, techniques used to recover the statistical information of the original data from the perturbed data become indispensable in data mining.

This paper reviews and exams the existing techniques for estimating (alternatively, reconstructing) the density function of the original data based on the data perturbed using the additive/multiplicative noise method. Our studies show that the techniques developed for noise-added data cannot replace the techniques for noise-multiplied data, though the two types of masked data could be mutually converted through data transformation. This conclusion might attract data providers’ attention.

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Notes

  1. 1.

    Some literature uses the term reconstructing. We will use them interchangeably in this paper.

  2. 2.

    See the discussion of the KEtal2003 Approach.

  3. 3.

    Other multiplicative noise distributions might be considered. Identifying a best multiplicative noise for masking the underlying data in terms of minimising the level of values disclosure risk and minimising the original data utility loss subject for future work.

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Acknowledgements

Part of R code for implementing the AS2000 Approach was developed by Miss A. Fernando supported by the Winter Project Scholarship 2016, School of Mathematics and Applied Statistics, UoW.

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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 & Pavel N. Krivitsky

Authors
  1. Yan-Xia Lin
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  2. Pavel N. Krivitsky
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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., Krivitsky, P.N. (2018). Reviewing the Methods of Estimating the Density Function Based on Masked 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_16

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  • DOI: https://doi.org/10.1007/978-3-319-99771-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99770-4

  • Online ISBN: 978-3-319-99771-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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