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Reverse Mapping to Preserve the Marginal Distributions of Attributes in Masked Microdata

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Privacy in Statistical Databases (PSD 2014)

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

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Abstract

In this paper we describe a new procedure that is capable of ensuring that the marginal distributions of attributes in microdata masked with a masking mechanism end up being the same as the marginal distributions of attributes in the original data. We illustrate the application of the new procedure using several commonly used masking mechanisms.

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Author information

Authors and Affiliations

  1. Visiting Professor, Price College of Business, University of Oklahoma, Norman, OK, USA

    Krishnamurty Muralidhar

  2. Ardmore Chair and Professor of Information Systems, Spears College of Business, Oklahoma State University, Stillwater, OK, USA

    Rathindra Sarathy

  3. UNESCO Chair in Data Privacy, Dept. of Computer Engineering and Maths, Universitat Rovira i Virgili, Tarragona, Catalonia

    Josep Domingo-Ferrer

Authors
  1. Krishnamurty Muralidhar
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  2. Rathindra Sarathy
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  3. Josep Domingo-Ferrer
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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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Muralidhar, K., Sarathy, R., Domingo-Ferrer, J. (2014). Reverse Mapping to Preserve the Marginal Distributions of Attributes in Masked Microdata. 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_9

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  • DOI: https://doi.org/10.1007/978-3-319-11257-2_9

  • 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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