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Why Statistical Confidentiality?

  • George T. DuncanEmail author
  • Mark Elliot
  • Juan-José Salazar-González
Chapter
  • 1.4k Downloads
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

Confirming statistical confidentiality as vital to the stewardship of personal data, the United Nations set out Principle 6 of its Fundamental Principles of Official Statistics: Individual data collected by statistical agencies for statistical compilation, whether they refer to natural or legal persons, are to be strictly confidential and used exclusively for statistical purposes. Data stewardship is active on two fronts:

Keywords

Statistical Purpose Disclosure Risk American Indian Youth National Statistical Office Confidentiality Concern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bethlehem, J.G., Keller, W.J., Pannekoek, J.: Disclosure control of microdata. J. Am. Stat. Assoc. 85, 38–45 (1990)CrossRefGoogle Scholar
  2. Cecil, J.S.: Confidentiality legislation and the United States federal statistical system. J. Off. Stat. 9(2), 5 (1993)Google Scholar
  3. Dalenius, T.: Controlling invasion of privacy in surveys. Department of Development and Research, Statistics, Sweden (1988)Google Scholar
  4. Duncan, G.T.: Exploring the tension between privacy and the social benefits of governmental databases. In: Podesta, J., Shane, P.M., Leone, R.C.A (eds.) Little Knowledge: Privacy, Security, and Public Information after September 11, pp. 71–88. The Century Foundation, New York, NY (2004)Google Scholar
  5. Duncan, G.T., Jabine, T.B., de Wolf, V.A. (eds.): Panel on Confidentiality and Data Access, Committee on National Statistics, Commission on Behavioral and Social Sciences and Education, National Research Council and the Social Science Research Council, Private Lives and Public Policies: Confidentiality and Accessibility of Government Statistics. National Academy of Sciences, Washington, DC (1993)Google Scholar
  6. Duncan, G.T., Keller-McNulty, S.A., Stokes, S.L.: Disclosure risk vs. data utility: the R-U confidentiality map. Technical report LA-UR-01-6428, Los Alamos National Laboratory, Los Alamos, NM 2001Google Scholar
  7. Duncan, G.T., Lambert, D.: Disclosure-limited data dissemination (with discussion). J. Am. Stat. Assoc. 81(393), 10–28 (1986)CrossRefGoogle Scholar
  8. Duncan, G.T., Lambert, D.: The risk of disclosure for microdata. J. Bus. Econ. Stat. 7, 207–217 (1989)CrossRefGoogle Scholar
  9. Duncan, G.T., Pearson, R.W.: Enhancing access to microdata while protecting confidentiality: prospects for the future (with discussion). Stat. Sci. 6, 219–239 (1991)CrossRefGoogle Scholar
  10. Elliot, M.J., Dale, A.: Scenarios of attack: a data intruder’s perspective on statistical disclosure risk. Netherlands Official Stat. 14, 6–10 (1999)Google Scholar
  11. Fienberg, S.E.: Confidentiality and disclosure limitation. In: Kempf-Leonard, K. (ed.) Encyclopedia of Social Measurement, pp. 463–469. Elsevier, New York, NY (2005)Google Scholar
  12. Garfinkel, R., Gopal, R., Goes, P.: Privacy protection of binary confidential data against deterministic, stochastic, and insider attack. Manage. Sci. 48, 749–764 (2002)CrossRefGoogle Scholar
  13. Heitzig, J.: The Jackknife method: confidentiality protection for complex statistical analyses. Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality. Geneva, Switzerland, 9–11 November (2005)Google Scholar
  14. Jabine, T.B.: Procedures for restricted data access. J. Official Stat. 9(2), 537–589 (1993a)Google Scholar
  15. Jabine, T.B.: Statistical disclosure limitation practices of United States statistical agencies. J. Official Stat. 9(2), 427–454 (1993b)Google Scholar
  16. Little, R.J.A.: Statistical analysis of masked data. J. Official Stat. 9(2), 407–426 (1993)Google Scholar
  17. National Research Council: Expanding access to research data: reconciling risks and opportunities. Panel on Data Access for Research Purposes, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. The National Academies Press, Washington, DC (2005)Google Scholar
  18. Paass, G.: Disclosure risk and disclosure avoidance for microdata. J. Bus. Econ. Stat. 6(4), 487–500 (1988)CrossRefGoogle Scholar
  19. Rasinski, K.A., Wright, D.: Practical aspects of disclosure analysis, of significance: a. Topical J. Assoc. Public Data Users 2(1), 35–41 (2000)Google Scholar
  20. Singer, E., Mathiowetz, N.A., Couper, M.P.: The impact of privacy and confidentiality concerns on survey participation: the case of the 1990 U.S Census. Public Opin. Q. 57(4), (Winter 1993), 465–482 (1993)CrossRefGoogle Scholar
  21. Singer, E., Van Hoewyk, J., Neugebauer, R.J.: Attitudes and behavior – the impact of privacy and confidentiality concerns on participation in the 2000 census. Public Opin. Q. 67, 368–384 (2003)CrossRefGoogle Scholar
  22. Stigler, S.M.: Adolphe Quetelet. Encyclopedia of Statistical Sciences. Wiley, New York, NY (1986)Google Scholar
  23. Greenberg, B.V.: Disclosure avoidance research at the census Bureau. Proceedings of the Bureau of the Census Sixth Annual Research Conference, Bureau of the Census, Washington, DC, pp. 144–166 1990Google Scholar
  24. McCaa, R., Odinga, A.: Statistical confidentiality and the construction of anonymized public use census samples: a draft proposal for the Kenyan Microdata for 1989. Paper presented to Social Science History Annual Convention, Chicago, IL, 15–18 November 2001Google Scholar
  25. National Research Council: Putting people on the map: protecting confidentiality with linked social-spatial data. Panel on confidentiality issues arising from the integration of remotely sensed and self-identifying data. In: Gutmann, M.P., Stern, P.C. (eds.) Committee on the Human Dimensions of Global Change, Division of Behavioral and Social Sciences and Education. The National Academies Press, Washington, DC. http://books.nap.edu/catalog.php?record_id=11865 (2007)
  26. Eurostat Manual of Business Statistics: http://unstats.un.org/unsd/EconStatKB/Attachment252.aspx (2005)
  27. Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Mining Knowl. Discov. 11(2), 195–212 (2005)CrossRefMathSciNetGoogle Scholar
  28. Duncan, G.T.: Appendix E: Confidentiality and data access issues for institutional review boards. In: Citro, C.F., Ilgen, D.R., Marrett, C.B. (eds.) Protecting Participants and Facilitating Social and Behavioral Research, pp. 235–252. National Research Council, Washington, DC (2003)Google Scholar
  29. Barabba, V.P., Kaplan, D.L.: U.S. Census Bureau Statistical Techniques to Prevent Disclosure – The Right to Privacy vs. the Need to Know. Paper presented at the 40th Session of the international Statistical Institute, Warsaw, 1975Google Scholar

Copyright information

© Springer New York 2011

Authors and Affiliations

  • George T. Duncan
    • 1
    Email author
  • Mark Elliot
    • 2
  • Juan-José Salazar-González
    • 3
  1. 1.Carnegie Mellon UniversitySanta FeUSA
  2. 2.University of ManchesterManchesterUK
  3. 3.University of La LagunaLa LagunaSpain

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