Encyclopedia of Database Systems

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

Information Loss Measures

  • Josep Domingo-FerrerEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1505


Data utility measures


Defining what a generic information loss measure is can be a tricky issue. Roughly speaking, it should capture the amount of information loss for a reasonable range of data uses. It will be said that there is little information loss if the protected dataset is analytically valid and interesting according to the following definitions by Winkler [4]:

A protected microdata set is analytically valid if it approximately preserves the following with respect to the original data (some conditions apply only to continuous attributes):
  1. 1.

    Means and covariances on a small set of subdomains (subsets of records and/or attributes)

  2. 2.

    Marginal values for a few tabulations of the data

  3. 3.

    At least one distributional characteristic


A microdata set is analytically interesting if six attributes on important subdomains are provided that can be validly analyzed.

More precise conditions of analytical validity and analytical interest cannot be stated without...
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Recommended Reading

  1. 1.
    Domingo-Ferrer J, Mateo-Sanz JM, Torra V. Comparing SDC methods for microdata on the basis of information loss and disclosure risk. In: Proceedings of the Joint Conferences on New Techniques and Technologies for Statistics and Exchange of Technology and Know-How; 2001. p. 807–26.Google Scholar
  2. 2.
    Domingo-Ferrer J, Torra V, et al. Disclosure protection methods and information loss for microdata. In: Doyle P, Lane JI, Theeuwes JJM, Zayatz L, editors. Confidentiality, disclosure and data access: theory and practical applications for statistical agencies. Amsterdam: Elsevier; 2001. p. 91–110.Google Scholar
  3. 3.
    Mateo-Sanz JM, Domingo-Ferrer J, Sebé F. Probabilistic information loss measures in confidentiality protection of continuous microdata. Data Min Knowl Discov. 2005;11(2):181–93.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Winkler WE. Re-identification methods for evaluating the confidentiality of analytically valid microdata. Res Off Stat. 1998;1(2):50–69.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Universitat Rovira i VirgiliTarragonaSpain

Section editors and affiliations

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