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


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methodology consisting of replacing some table entries by appropriate intervals containing the actual value of the unpublished cells.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 Also, the new method provides automatically auditing ranges for each unpublished cell, thus saving an often time-consuming task to the statistical office while increasing the information explicitly provided with the table.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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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Matteo Fischetti
  • Juan-José Salazar-González

There are no affiliations available

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