Skip to main content

Matrix Masking

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

Synonyms

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

Definition

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

$$ Z\to AZB+C, $$
(1)

where A is a matrix that operates on the n cases, B is a matrix that operates on the p variables, and C is a matrix that adds perturbations or noise.

Matrix masking includes a wide variety of standard approaches to SDL: (i) adding noise, i.e., the C in matrix masking transformation of equation [1]; (ii) releasing a subset of observations (delete rows from Z), i.e., sampling; (iii) cell suppression for cross-classifications;...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  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. 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. Duncan GT, Pearson RB. Enhancing access to microdata while protecting confidentiality: prospects for the future (with discussion). Stat Sci. 1991;6(3):219–39.

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephen E. Fienberg .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

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

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Fienberg, S.E., Jin, J. (2018). Matrix Masking. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1535

Download citation

Publish with us

Policies and ethics