Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Nonperturbative Masking Methods

  • Josep Domingo-FerrerEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1500


Non-perturbative masking


Non-perturbative masking methods are SDC methods for microdata protection which do not alter data; rather, they produce partial suppressions or reductions of detail in the original dataset. Sampling, global recoding, top and bottom coding and local suppression are examples of non-perturbative masking methods.

Key Points

  1. 1.

    Sampling is a non-perturbative masking method for statistical disclosure control of microdata. Instead of publishing the original microdata file, what is published is a sample S of the original set of records. Sampling methods are suitable for categorical microdata, but for continuous microdata they should probably be combined with other masking methods. The reason is that sampling alone leaves a continuous attribute Vi unperturbed for all records in S. Thus, if attribute Vi is present in an external administrative public file, unique matches with the published sample are very likely: indeed, given a continuous attribute V

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Recommended Reading

  1. 1.
    DeWaal AG, Willenborg LCRJ. Global recodings and local suppressions in microdata sets. In: Proceedings of the Statistics Canada Symposium; 1995. p. 121–32.Google Scholar
  2. 2.
    Hundepool A, Domingo-Ferrer J, Franconi L, Giessing S, Lenz R, Longhurst J, Schulte-Nordholt E, Seri G, DeWolf P-P. Handbook on statistical disclosure control (version 1.0). Eurostat (CENEX SDC Project Deliverable); 2006. http://neon.vb.cbs.nl/CENEX/
  3. 3.
    Hundepool A, Van de Wetering A, Ramaswamy R, Franconi F, Polettini S, Capobianchi A, DeWolf P-P, Domingo-Ferrer J, Torra V, Brand R, Giessing S. μ-ARGUS user’s manual version 4.1, Feb 2007. http://neon.vb.cbs.nl/CASC
  4. 4.
    Willenborg L, DeWaal T. Elements of statistical disclosure control. New York: Springer; 2001.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universitat Rovira i VirgiliTarragona, CataloniaSpain

Section editors and affiliations

  • Elena Ferrari
    • 1
  1. 1.DiSTAUniv. of InsubriaVareseItaly