Statistics and Computing

, Volume 13, Issue 1, pp 13–21 | Cite as

Partial cell suppression: A new methodology for statistical disclosure control

  • Matteo Fischetti
  • Juan-José Salazar-González
Article

Abstract

In this paper we address the problem of protecting confidentiality in statistical tables containing sensitive information that cannot be disseminated. This is an issue of primary importance in practice. Cell Suppression is a widely-used technique for avoiding disclosure of sensitive information, which consists in suppressing all sensitive table entries along with a certain number of other entries, called complementary suppressions. Determining a pattern of complementary suppressions that minimizes the overall loss of information results into a difficult (i.e., \(\mathcal{N}\mathcal{P}\)-hard) optimization problem known as the Cell Suppression Problem. We propose here a different protection methodology consisting of replacing some table entries by appropriate intervals containing the actual value of the unpublished cells. We call this methodology Partial Cell Suppression, as opposed to the classical “complete” cell suppression. Partial cell suppression has the important advantage of reducing the overall information loss needed to protect the sensitive information. Also, the new method provides automatically auditing ranges for each unpublished cell, thus saving an often time-consuming task to the statistical office while increasing the information explicitly provided with the table. Moreover, we propose an efficient (i.e., polynomial-time) algorithm to find an optimal partial suppression solution. A preliminary computational comparison between partial and complete suppression methologies is reported, showing the advantages of the new approach. Finally, we address possible extensions leading to a unified complete/partial cell suppression framework.

cell suppression statistical data protection statistical disclosure control confidentiality linear programming 

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References

  1. Cox L.H. 1980. Suppression methodology and statistical disclosure control. Journal of the American Statistical Association 75: 377–385.Google Scholar
  2. Cox L.H. 1995. Network models for complementary cell suppression. Journal of the American Statistical Association 90: 1453–1462.Google Scholar
  3. Crowder H.P., Johnson E.L., and Padberg M.W. 1983. Solving large-scale zero-one linear programming problems. Operations Research 31: 803–834.Google Scholar
  4. Carvalho F.D., Dellaert N.P., and Osório M.S. 1994. Statistical disclosure in two-dimensional tables: General tables. Journal of the American Statistical Association 89: 1547–1557.Google Scholar
  5. Dellaert N.P. and Luijten W.A. 1996. Statistical disclosure in general three-dimensional tables. Technical Paper TI 96-114/9, Tibergen InstituteGoogle Scholar
  6. Fischetti M.and Salazar J.J. 1999. Models and algorithms for the 2-dimensional cell suppression problem in statistical disclosure control. Mathematical Programming 84: 283–312Google Scholar
  7. Fischetti M.and Salazar J.J. 2000. Models and algorithms for optimizing cell suppression problem in tabular data with linear constraints. Journal of the American Statistical Association 95: 916–928.Google Scholar
  8. Geurts J. 1992. Heuristics for cell suppression in tables. Technical Paper, Netherlands Central Bureau of Statistics, Voorburg.Google Scholar
  9. Gusfield D. 1988. Agraph theoretic approach to statistical data security. SIAM Journal on Computing 17: 552–571.Google Scholar
  10. Kao M.Y. 1996. Data security equals graph connectivity. SIAM Journal on Discrete Mathematics 9: 87–100.Google Scholar
  11. Kelly J.P. 1990. Confidentiality protection in two and three-dimensional tables. Ph.D. dissertation, University of Maryland, College Park, Maryland.Google Scholar
  12. Kelly J.P., Golden B.L., and Assad A.A. 1992. Cell suppression: Disclosure protection for sensitive tabular data. Networks 22: 397–417.Google Scholar
  13. Nemhauser G.L. and Wolsey L.A. 1988. Integer and Combinatorial Optimization. John Wiley & Sons, New York.Google Scholar
  14. Robertson D.A. 1994. Cell suppression at statistics Canada. In: Proceedings of the Second International Conference on Statistical Confidentiality, Luxembourg.Google Scholar
  15. Sande G. 1984. Automated cell suppression to preserve confidentiality of business statistics. Statistical Journal of the United Nations ECE 2: 33–41.Google Scholar
  16. Willenborg L.C.R.J. and De Waal T. 1996. Statistical Disclosure Control in Practice. Lecture Notes in Statistics 111. Springer, New York.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Matteo Fischetti
  • Juan-José Salazar-González

There are no affiliations available

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