Encyclopedia of Database Systems

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

Statistical Disclosure Limitation for~Data~Access

  • Stephen E. FienbergEmail author
  • Jiashun Jin
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1046


Confidentiality protection; Multiplicity; Privacy protection; Restricted data; Risk-utility tradeoff


Statistical Disclosure Limitation refers to the broad array of methods used to protect confidentiality of statistical data, i.e., fulfilling an obligation to data providers or respondents not to transmit their information to an unauthorized party. Data Access refers to complementary obligations of statistical agencies and others to provide information for statistical purposes without violating promises of confidentiality.

Historical Background

Starting in the early twentieth century, U.S. government statistical agencies worked to develop approaches for the protection of the confidentiality of data gathered on individuals and organizations. As such agencies also have a public obligation to use the data for the public good, they have developed both a culture of confidentiality protection and a set of statistical techniques to assure that data are released in a form...

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Recommended Reading

  1. 1.
    Abramovich F, Benjamini Y, Donoho D, Johnstone I. Adapting to unknown sparsity by controlling the false discovery rate. Ann Stat. 2006;34(2):584–653.MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Anderson M, William SW. Challenges to the confidentiality of U.S. federal statistics, 1910–1965. J Off Stat. 2007;23(1):1–34.Google Scholar
  3. 3.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc B. 1995;57(1):289–300.MathSciNetzbMATHGoogle Scholar
  4. 4.
    Dalenius T. Towards a methodology for statistical disclosure control. Statist Tidskrift. 1977;5(429–444): 2–1.Google Scholar
  5. 5.
    Donoho D, Jin J. Higher Criticism for detecting sparse heterogeneous mixtures. Ann Stat. 2004;32(3):962–94.MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Doyle P, Lane JL, Theeuwes Jules JM, Zayatz LV, editors. Confidentiality, disclosure and data access: theory and practical application for statistical agencies. New York: Elsevier; 2001.Google Scholar
  7. 7.
    Fienberg SE. Confidentiality, privacy and disclosure limitation. In: Encyclopedia of social measurement, vol. 1. San Diego: Academic Press; 2005. p. 463–9.CrossRefGoogle Scholar
  8. 8.
    Fienberg SE, Makov UE. Confidentiality, uniqueness and disclosure limitation for categorical data. J Off Stat. 1998;14(4):485–502.zbMATHGoogle Scholar
  9. 9.
    Fienberg SE, Makov UE, Sanil AP. A Bayesian approach to data disclosure: optimal intruder behavior for continuous data. J Off Stat. 1997;13(1):75–89.Google Scholar
  10. 10.
    Fienberg SE, Makov UE, Steele RJ. Disclosure limitation using perturbation and related methods for categorical data (with discussion). J Off Stat. 1998;14(4):485–502.Google Scholar
  11. 11.
    Fienberg SE, Slavkovic AB. Preserving the confidentiality of categorical statistical databases when releasing information for association rules. Data Min Knowl Discov. 2005;11(2):155–80.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hertzog TN, Scheuren FJ, Winkler WE. Data quality and record linkage techniques. New York: Springer-Verlag; 2007.zbMATHGoogle Scholar
  13. 13.
    Lambert D. Measures of disclosure risk and harm. J Off Stat. 1993;9(2):313–31.Google Scholar
  14. 14.
    Raghunathan TE, Reiter J, Rubin DB. Multiple imputation for statistical disclosure limitation. J Off Stat. 2003;19(1):1–16.Google Scholar
  15. 15.
    Warren S, Brandeis L. The right to privacy. Harvard Law Rev. 1890;4(5):193–220.CrossRefGoogle Scholar
  16. 16.
    Willenborg L, de Waal T. Elements of statistical disclosure control, vol. 155. New-York: Lecture Notes in Statistics Springer-Verlag; 2001.zbMATHGoogle 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