Skip to main content

Enabling Statistical Analysis of Suppressed Tabular Data

  • Conference paper
Privacy in Statistical Databases (PSD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8744))

Included in the following conference series:

Abstract

For decades, NSOs have usedcomplementary cell suppression for disclosure limitation of tabular data, magnitude data in particular. Indications of its continued use abound, even though suppression thwarts statistical analysis of both the expert and the novice. We introduce methods for creating alternative tables that the NSO can release unsuppressed, while ensuring within statistical certainty that their analysis is conformal with analysis of the original.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Cox, L.H.: Suppression methodology and statistical disclosure control. Journal of the American Statistical Association 75(370), 377–385 (1980)

    Article  MATH  Google Scholar 

  2. Cox, L.H.: Disclosure risk for tabular economic data. In: Doyle, P., Lane, J., Theeuwes, J., Zayatz, L. (eds.) Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, ch. 8, pp. 167–183. Elsevier, New York (2001)

    Google Scholar 

  3. Cox, L.H., Karr, A.F., Kinney, S.: Risk-Utility paradigms for statistical disclosure limitation: How to think but not how to act (with discussion). International Statistical Review 2, 160–183 (2011) (with discussion)

    Google Scholar 

  4. Fellegi, I.P.: On the question of statistical confidentiality. Journal of the American Statistical Association 67, 7–18 (1972)

    Article  MATH  Google Scholar 

  5. Cox, L.H.: Network models for complementary cell suppression. Journal of the American Statistical Association, 90(432), 1153–1162 (1995)

    Google Scholar 

  6. Dalenius, T.: Towards a methodology for statistical disclosure control. Statist Tidskrift 5, 429–444 (1977)

    Google Scholar 

  7. Federal Committee on Statistical Methodology. Report on Disclosure Limitation Methodology—Statistical Policy Working Paper 22. Office Management & Budget, Washington, DC (Rev: 2006) (1994)

    Google Scholar 

  8. U.S. Department of Commerce. Report on Statistical Disclosure and Disclosure Limitation—Statistical Policy Working Paper 2. Office of Statistical Policy and Standards Washington, DC (1978)

    Google Scholar 

  9. Kelly, J.P., Golden, B.L., Assad, A.A.: Cell suppression: Disclosure protection for sensitive tabular data. Networks 22, 397–417 (1992)

    Article  MATH  Google Scholar 

  10. Cox, L.H.: A data quality and data confidentiality assessment of complementary cell suppression. In: Domingo-Ferrer, J., Saygın, Y. (eds.) PSD 2008. LNCS, vol. 5262, pp. 13–23. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Cox, L.H.: Vulnerability of complementary cell suppression to intruder attack. Journal of Privacy and Confidentiality 1(2), 235–251 (2009), http://jpc.stat.cmu.edu

    Google Scholar 

  12. Fischetti, M., Salazar, J.J.: Models and algorithnms for optimizing cell suppression in tabular data with linear constraints. Journal of the American Statistical Association 95, 916–928 (2000)

    Article  Google Scholar 

  13. Fischetti, M., Salazar, J.J.: Solving the cell suppression problem in tabular data with linear constraints. Management Science 47(7), 1008–1026 (2001)

    Article  MATH  Google Scholar 

  14. Cox, L.H.: Contingency tables of network type: Models, Markov basis and applications. Statistica Sinica 17(4), 1371–1393 (2007)

    MATH  MathSciNet  Google Scholar 

  15. Dobra, A., Fienberg, S.E.:  Bounds for cell entries in contingency tables given marginal totals and decomposable graphs. Proceedings of the National Academy of Sciences 97(22), 11885–11892 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  16. Salazar, J.J.: Statistical confidentiality: Optimization techniques to protect tables. Computers & Operations Research 35, 1638–1651 (2008)

    Article  Google Scholar 

  17. Cox, L.H., Kelly, J.P., Patil, R.: Balancing quality and confidentiality for multivariate tabular data. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 87–98. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  18. Cox, L.H., Orelien, J.G., Shah, B.V.: A method for preserving statistical distributions subject to controlled tabular adjustment. In: Domingo-Ferrer, J., Franconi, L. (eds.) PSD 2006. LNCS, vol. 4302, pp. 1–11. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  19. Raghunathan, T.E., Reiter, J.P., Rubin, D.B.: Multiple imputation for statistical disclosure limitation. Journal of Official Statistics 19, 1–16 (2003)

    Google Scholar 

  20. Reiter, J.P.: Satisfying disclosure restrictions with synthetic data sets. Journal of Official Statistics 18, 531–544 (2002)

    Google Scholar 

  21. Reiter, J.P.: Releasing multiply-imputed, synthetic public use microdata: An illustration and empirical study. Journal of the Royal Statistical Society, Series A 168, 185–205 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  22. Wooton, J., Fraser, B.: A review of confidentiality protections for statistical tables, with special reference to the differencing problem. Australian Bureau of Statistics Methodology Report 1352.0.55.072. Australian Bureau of Statistics, Canberra (2005)

    Google Scholar 

  23. Isserman, A.M., Westervelt, J.: 1.5 million missing numbers: overcoming employment suppression in County Business Patterns data. International Regional Science Review 29(3), 311–335 (2006)

    Article  Google Scholar 

  24. Cox, L.H.: On properties of multi-dimensional statistical tables. Journal of Statistical Planning and Inference 17(2), 251–273 (2003)

    Article  Google Scholar 

  25. Cox, L.H.: Inference control problems in statistical database query systems. In: Farkas, C., Samarati, P. (eds.) Research Directions in Data and Applications Security, pp. 1–13. Kluwer, Boston (2004)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Cox, L.H. (2014). Enabling Statistical Analysis of Suppressed Tabular Data. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11257-2_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11256-5

  • Online ISBN: 978-3-319-11257-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics