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
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;...
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Recommended Reading
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.
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.
Duncan GT, Pearson RB. Enhancing access to microdata while protecting confidentiality: prospects for the future (with discussion). Stat Sci. 1991;6(3):219–39.
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.
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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
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DOI: https://doi.org/10.1007/978-1-4614-8265-9_1535
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