Protecting Tabular Data

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


Familiar to all of us, a statistical table displays aggregate information that is classified according to categories. Even in this age of electronic dissemination, tables remain important data products. In the past, DSOs published these tables in paper form as large statistical abstracts. Today, many DSOs provide users with an online capability for special tabulations—an easy way of generating their own tables. This table server mode of dissemination has been implemented as American Fact-Finder from the US Bureau of the Census, Neighbourhood Statistics from the UK Office for National Statistics, Multidimensional Statistical Database (BME) from the Brazilian Institute of Geography and Statistics, and StatLine from Statistics Netherlands, as well as other efforts of national statistical offices.


Data Utility Cell Suppression Marginal Cell Output Pattern Internal Cell 
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.


  1. Bacharach, M.: Matrix rounding problem. Manage. Sci. 9, 732–742 (1966)CrossRefMathSciNetGoogle Scholar
  2. Carvalho, F.D., Dellaert, N.P., Osório, M.S.: Statistical disclosure in two-dimensional tables: general tables. J. Am. Stat. Assoc. 89, 1547–1557 (1994)CrossRefzbMATHGoogle Scholar
  3. Causey, B.D., Cox, L.H., Ernst, L.R.: Applications of transportation theory to statistical problems. J. Am. Stat. Assoc. 80, 903–909 (1985)CrossRefMathSciNetGoogle Scholar
  4. Cox, L.H.: Suppression methodology and statistical disclosure control. J. Am. Stat. Assoc. 75, 377–385 (1980)CrossRefzbMATHGoogle Scholar
  5. Cox, L.H.: Linear sensitivity measures and statistical disclosure control. J. Stat. Plann. Inference 5, 153–164 (1981)CrossRefzbMATHGoogle Scholar
  6. Cox, L.H.: Network models for complementary cell suppression. J. Am. Stat. Assoc. 90, 1453–1462 (1995)CrossRefzbMATHGoogle Scholar
  7. Cox, L.H., Kelly, J.P., Patil, R.: Balancing quality and confidentiality for multivariate tabular data. In: Domingo-Ferrer, J., Torra, V. (eds.) Privacy in Statistical Databases. Lecture Notes in Computer Science, vol. 3050, pp. 87–98. Springer, New York, NY (2004)Google Scholar
  8. Dellaert, N.P., Luijten, W.A.: Statistical disclosure in general three-dimensional tables. Stat. Neerl. 53, 197–221 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  9. Domingo-Ferrer, J., Torra, V.: Disclosure risk assessment in statistical data protection. J. Comput. Appl. Math. 164–165, 285–293 (2004)CrossRefMathSciNetGoogle Scholar
  10. Doyle, P., Lane, J., Theeuwes, J., Zayatz, L.: Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies. Elsevier Science, Amsterdam (2001)Google Scholar
  11. 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
  12. Duncan, G.T., Roehrig, S.F.: Reconciling information privacy and information access in a globalized technology society. In: Erickson, J. (ed.) Database Technologies: Concepts, Methodologies, Tools, and Applications, pp. 1823–1843. IGI Global, Hershey, PA (2007)Google Scholar
  13. Evans, T., Zayatz, L., Slanta, J.: Using noise for disclosure limitation of establishment tabular data. J. Official Stat. 14(4), 537–551 (1998)Google Scholar
  14. Fischetti, M., Salazar, J.J.: Computational experience with the controlled rounding problem in statistical disclosure control. J. Official Stat. 14(4), 553–565 (1998)Google Scholar
  15. Fischetti, M., Salazar, J.J.: Models and algorithms for the 2-dimensional cell suppression problem in statistical disclosure control. Math. Program. 84, 283–312 (1999)zbMATHMathSciNetGoogle Scholar
  16. Fischetti, M., Salazar, J.J.: Partial cell suppression: a new methodology for statistical disclosure control. Stat. Comput. 13, 13–21 (2003)CrossRefMathSciNetGoogle Scholar
  17. Geurts, J.: Heuristics for cell suppression in tables. Master’s thesis, Netherlands Central Bureau of Statistics, Voorburg (1992)Google Scholar
  18. Griffin, R.A., Navarro, A., Flores-Baez, L.: Disclosure avoidance for the 1990 Census. Proceedings of the Section on Survey Research Methods, American Statistical Association, Alexandria, VA, pp. 516–521 (1989)Google Scholar
  19. Jewett, R.: Disclosure analysis for the 1992 economic census. Technical report, U.S. Bureau of the Census, Washington, DC 1993Google Scholar
  20. Kelly, J.P.: Confidentiality protection in two and three-dimensional tables. Ph. D. thesis, University of Maryland, College Park, MD (1990)Google Scholar
  21. Kelly, J.P., Golden, B.L., Assad, A.A.: Using simulated annealing to solve controlled rounding problems. ORSA J. Comput. 2, 174–185 (1990)zbMATHGoogle Scholar
  22. Kelly, J.P., Golden, B.L., Assad, A.A.: Cell suppression: disclosure protection for sensitive tabular data. Networks 22, 397–417 (1992)CrossRefzbMATHGoogle Scholar
  23. Kelly, J.P., Golden, B.L., Assad, A.A.: Large-scale controlled rounding using tabu search with strategic oscillation. Ann. Oper. Res. 41, 69–84 (1993)CrossRefzbMATHGoogle Scholar
  24. Loeve, A.: Notes on sensitivity measures and protection levels. Technical report, Statistics Netherlands, The Hague (2001)Google Scholar
  25. Navarro, A., Flores-Baez, L., Thompson, J.: Results of data switching simulation. Presented at the Spring meeting of the American Statistical Association and Population Statistics Census Advisory Committees, Washington, DC (1988)Google Scholar
  26. Robertson, D.A.: Cell suppression at statistics Canada. Proceedings of the Second International Conference on Statistical Confidentiality, Luxembourg 1994Google Scholar
  27. Robertson, D.A.: Improving Statistics Canada’s cell suppression software. Technical report, Statistics Canada, Ottawa, ON 2000Google Scholar
  28. Robertson, D.A., Ethier, R.: Cell suppression: experience and theory. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases: From Theory to Practice. Lecture Notes in Computer Science, vol. 2316, pp. 8–20. Springer, New York, NY (2002)Google Scholar
  29. Salazar, J.J.: A unified mathematical programming framework for different statistical disclosure limitation methods. Oper. Res. 53(3), 819–829 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  30. Salazar, J.J.: Controlled rounding and cell perturbation: statistical disclosure limitation methods for tabular data. Math. Program. 105(2–3), 251–274 (2006a)zbMATHMathSciNetGoogle Scholar
  31. Salazar, J.J.: A new approach to round tabular data. Lecture Notes in Computer Science 4302, 25–34 (2006b)CrossRefGoogle Scholar
  32. Salazar, J.J.: Statistical confidentiality: optimization techniques to protect tables. Comput. Oper. Res. 35, 1638–1651 (2008)CrossRefzbMATHGoogle Scholar
  33. Salazar, J., Lowthian, P., Young, C., Merola, G., Bond, S., Brown, D.: Getting the best results in controlled rounding with the least effort. In: Domingo-Ferrer, J. (ed.) Privacy in Statistical Databases. Lecture Notes in Computer Science, vol. 3050, pp. 58–72. Springer, New York, NY (2004)CrossRefGoogle Scholar
  34. Sande, G.: Automated cell suppression to preserve confidentiality of business statistics. Stat. J. United Nat. ECE 2, 33–41 (1984)Google Scholar
  35. Sande, G.: Structure of the ACS automated cell suppression system, In Statistical Data Confidentiality. Proceedings of the Joint Eurostat/UN-ECE Work Session on Statistical Confidentiality, Skopje, pp. 105–121 (1999)Google Scholar
  36. Smith, D., Elliot, M.J.: A measure of disclosure risk for tables of counts. Trans. Data Priv. 1(1), 34–52 With Smith, D. (2008)MathSciNetGoogle Scholar
  37. Willenborg, L.C.R., de Waal, T.: Elements of Statistical Disclosure Control. Lecture Notes in Statistics, vol. 155. Springer, New York, NY (2001)CrossRefzbMATHGoogle Scholar
  38. Zayatz, L.V.: Using linear programming methodology for disclosure avoidance purposes, Technical report, U.S. Bureau of the Census. Research Report RR-92/02, Washington, DC 1992Google Scholar
  39. Fischetti, M., Salazar, J.J.: Models and algorithms for optimizing cell suppression in tabular data with linear constraints. J. Am. Stat. Assoc. 95(451), 916–928 (2000)CrossRefGoogle Scholar
  40. Salazar, J.J.: Branch-and-cut versus cut-and-branch algorithms for cell suppression. Lecture Notes in Computer Science 6344, 29–40 (2010)CrossRefGoogle 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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