Controlled rounding and cell perturbation: statistical disclosure limitation methods for tabular data
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Rounding methods are common techniques in many statistical offices to protect sensitive information when publishing data in tabular form. Classical versions of these methods do not consider protection levels while searching patterns with minimum information loss, and therefore typically the so-called auditing phase is required to check the protection of the proposed patterns. This paper presents a mathematical model for the whole problem of finding a protected pattern with minimum loss of information, and proposes a branch-and-cut algorithm to solve it. It also describes a new methodology closely related to the classical Controlled Rounding methods but with several advantages. The new methodology is named Cell Perturbation and leads to a different optimization problem which is simpler to solve than the previous problem. This paper presents a cutting-plane algorithm for finding an exact solution of the new problem, which is a pattern guaranteeing the same protection level requirements but with smaller loss of information when compared with the classical Controlled Rounding optimal patterns. The auditing phase is unnecessary on the solutions generated by the two algorithms. The paper concludes with computational results on real-world instances and discusses a modification in the objective function to guarantee statistical properties in the solutions.
KeywordsStatistical disclosure Control Controlled rounding Integer linear Programming
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- 5.Dandekar, R.A.: Maximum Utility-Minimum Information Loss Table Server Design for Statistical Disclosure Control of Tabular Data. In: Domingo-Ferrer, J. (ed.), Privacy in Statistical Databases. Lecture Notes in Computer Science 3050, Springer, 2004, pp. 121–135Google Scholar
- 6.Duncan, G.T., Fienberg, S.E., Krishnan, R., Padman, R., Roehrig, S.F.: ``Disclosure Limitation Methods and Information Loss for Tabular Data. In: Doyle, P., Lane, J., Theeuwes, J., Zayatz, L. (eds.), Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies. Elsevier Science, 2001, pp. 135–166Google Scholar
- 7.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
- 8.Fischetti, M., Salazar, J.J.: Solving the Cell Suppression Problem on Tabular Data with Linear Constraints. Management Sci. 47, 1008–1026 (2000)Google Scholar
- 10.Jewett, R.: Disclosure Analysis for the 1992 Economic Census. Internal report, U.S. Bureau of the Census, Washington, 1993Google Scholar
- 13.Salazar, J. J.: Controlled Rounding and Cell Perturbation: technical details. Internal report, University of La Laguna, Tenerife, 2004Google Scholar
- 14.Sande, G.: Automated Cell Suppression to preserve confidentiality of business statistics. Statistical Journal of the United Nations ECE 2, 1984, pp. 33–41Google Scholar
- 15.Willenborg, L.C.R.J., de Waal, T.: Elements of Statistical Disclosure Control. Lecture Notes in Statistics 155, Springer, 2001Google Scholar