Experiences on model based disclosure limitation

Article type: Research Article

Authors: Franconi, Luisa^{; 1; 2} | Capobianchi, Alessandra | Polettini, Silvia | Seri, Giovanni

Affiliations: Istat, Metodologia di base per la produzione statistica, Via C. Balbo 16, 00184 Roma, Italy. E-mail: [email protected]; [email protected]; [email protected]; [email protected]

Note: [1] This work was partially supported by the European Union project IST-2000-25069 CASC on Computational Aspects of Statistical Confidentiality.

Note: [2] The views expressed are those of the authors and do not necessarily reflect the policies of the Istituto Nazionale di Statistica.

Abstract: National statistical institutes routinely apply imputation methods based on statistical models to survey nonresponses. This area of research is very important because it is at the basis of the production of economic data which are as accurate as possible. The idea is to take stock of the experiences gathered in the field of imputation methodology and to try to bridge the gap between this area of research and statistical disclosure limitation. In this paper we review our experiences on model based disclosure limitation techniques. In general, these techniques substitute the observed value of a certain variable with the estimated value via a statistical model. In particular, we discuss the problems encountered and the possible solutions found with two different models: a regression tree model [2] for a categorical variable [17] and a hierarchical model for a continuous variable [9].

Keywords: business microdata, confidentiality, hierarchical models, regression trees

DOI: 10.3233/SJU-2001-18408

Journal: Statistical Journal of the United Nations Economic Commission for Europe, vol. 18, no. 4, pp. 337-344, 2001