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International Conference on Privacy in Statistical Databases

PSD 2014: Privacy in Statistical Databases pp 200–212Cite as

v-Dispersed Synthetic Data Based on a Mixture Model with Constraints

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8744))

Abstract

In this paper a new approach is proposed for the generation of synthetic microdata which reduces attribute disclosure for continuous variables. First, we define a metric of attribute disclosure which is called v-dispersion. This metric quantifies the risk based on the volume of the multidimensional confidence regions for the original data values. Next we describe a method that satisfies the requirements of v-dispersion. This method is based on a mixture model with constraints on parameters of components’ spread. Experiments with real data show that the proposed approach compares very favorably with other methods of disclosure limitation for continuous microdata in terms of utility and risk.

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Author information

Authors and Affiliations

  1. National Center for Health Statistics, 3311 Toledo Rd, Hyatsville, MD, 20782, U.S.A.

    Anna Oganian

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  1. Anna Oganian
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007, Tarragona, Catalonia

    Josep Domingo-Ferrer

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Oganian, A. (2014). v-Dispersed Synthetic Data Based on a Mixture Model with Constraints. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-11257-2_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11256-5

  • Online ISBN: 978-3-319-11257-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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