Balancing Quality and Confidentiality for Multivariate Tabular Data

  • Lawrence H. Cox
  • James P. Kelly
  • Rahul Patil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3050)

Abstract

Absolute cell deviation has been used as a proxy for preserving data quality in statistical disclosure limitation for tabular data. However, users’ primary interest is that analytical properties of the data are for the most part preserved, meaning that the values of key statistics are nearly unchanged. Moreover, important relationships within (additivity) and between (correlation) the published tables should also be unaffected. Previous work demonstrated how to preserve additivity, mean and variance in for univariate tabular data. In this paper, we bridge the gap between statistics and mathematical programming to propose nonlinear and linear models based on constraint satisfaction to preserve additivity and covariance, correlation, and regression coefficient between data tables. Linear models are superior than nonlinear models owing to simplicity, flexibility and computational speed. Simulations demonstrate the models perform well in terms of preserving key statistics with reasonable accuracy.

Keywords

Controlled tabular adjustment linear programming covariance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cox, L.H.: Suppression Methodology and Statistical Disclosure Control. Journal of the American Statistical Association 75, 377–385 (1980)MATHCrossRefGoogle Scholar
  2. 2.
    Fellegi, I.P.: On the Question of Statistical Confidentiality. Journal of the American Statistical Association 67, 7–18 (1972)MATHCrossRefGoogle Scholar
  3. 3.
    Dandekar, R.A., Cox, L.H.: Synthetic Tabular Data: An Alternative to Complementary Cell Suppression (2002) (manuscript)Google Scholar
  4. 4.
    Department, U.S.: of Commerce.: Statistical Disclosure and Disclosure Limitation Methods, Statistical Policy Working Paper 22, Federal. Committee on Statistical Methodology, Washington, DC (1994)Google Scholar
  5. 5.
    Cox, L.H.: Network Models for Complementary Cell Suppression. Journal of the American Statistical Association 90, 1153–1162 (1995)CrossRefGoogle Scholar
  6. 6.
    Cox, L.H., Dandekar, R.A.: A New Disclosure Limitation Method for Tabular Data that Preserves Data Accuracy and Ease of Use. In: Proceedings of the 2002 FCSM Statistical Policy Seminar, U.S. Office of Management and Budget, Washington, DC (2003) (in press)Google Scholar
  7. 7.
    Cox, L.H., Kelly, J.P.: Balancing Data Quality and Confidentiality for Tabular Data. In: Proceedings of the UNECE/Eurostat Work Session on Statistical Data Confidentiality, Luxembourg, April 7-9. Monographs of Official Statistics, Eurostat, Luxembourg (2003) (in press)Google Scholar
  8. 8.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Amsterdam (1997)MATHGoogle Scholar
  9. 9.
    Fischetti, M., Salazar-Gonzalez, J.J.: Models and Algorithms for Optimizing Cell Suppression in Tabular Data with Linear Constraints. Journal of the American Statistical Association 95, 916–928 (2000)CrossRefGoogle Scholar
  10. 10.
    Cox, L.H.: Discussion. ICES II: The Second International Conference on Establishment Surveys: Survey Methods for Businesses, Farms and Institutions, pp. 905–907. American Statistical Association, Alexandria (2000)Google Scholar
  11. 11.
    Cox, L.H., Kelly, J.P.: Controlled Tabular Adjustment: An Empirical Study of Heuristic and Optimal Methods (2004) (submitted)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Lawrence H. Cox
    • 1
  • James P. Kelly
    • 2
  • Rahul Patil
    • 2
  1. 1.National Center for Health Statistics, Centers for Disease Control and PreventionHyattsvilleUSA
  2. 2.OptTek Systems, Inc.BoulderUSA

Personalised recommendations