Encyclopedia of Database Systems

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

Matrix Masking

  • Stephen E. FienbergEmail author
  • Jiashun Jin
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1535


Adding noise; Data perturbation; Recodings; Sampling; Synthetic data


Matrix Masking refers to a class of statistical disclosure limitation (SDL) methods used to protect confidentiality of statistical data, transforming an n × p (cases by variables) data matrix Z through pre- and post-multiplication and the possible addition of noise.

Key Points

Duncan and Pearson [ 3] and many others subsequently categorize the methodology used for SDL in terms of transformations of an n × p (cases by variables) data matrix Z of the form
$$ Z\to AZB+C, $$
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Recommended Reading

  1. 1.
    Doyle P, Lane JI, Theeuwes JJM, Zayatz L, editors. Confidentiality, disclosure and data access: theory and practical application for statistical agencies. New York: Elsevier; 2001.Google Scholar
  2. 2.
    Duncan GT, Jabine TB, De Wolf VA, editors. Private lives and public policies. Report of the Committee on National Statistics’ panel on confidentiality and data access. Washington, DC: National Academy Press; 1993.Google Scholar
  3. 3.
    Duncan GT, Pearson RB. Enhancing access to microdata while protecting confidentiality: prospects for the future (with discussion). Stat Sci. 1991;6(3):219–39.CrossRefGoogle Scholar
  4. 4.
    Federal Committee on Statistical Methodology. Report on statistical disclosure limitation methodology, Statistical policy working paper 22. Washington, DC: U.S. Office of Management and Budget; 1994.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

Section editors and affiliations

  • Chris Clifton
    • 1
  1. 1.Dept. of Computer SciencePurdue UniversityWest LafayetteUSA