Abstract
We argue that any microdata protection strategy is based on a formal reference model. The extent of model specification yields “parametric”, “semiparametric”, or “nonparametric” strategies. Following this classification, a parametric probability model, such as a normal regression model, or a multivariate distribution for simulation can be specified. Matrix masking (Cox [2]), covering local suppression, coarsening, microaggregation (Domingo-Ferrer [8]), noise injection, perturbation (e.g. Kim [15]; Fuller [12]), provides examples of the second and third class of models. Finally, a nonparametric approach, e.g. use of bootstrap procedures for generating synthetic microdata (e.g. Dandekar et. al. [4]) can be adopted.
In this paper we discuss the application of a regression based imputation procedure for business microdata to the Italian sample from the Community Innovation Survey. A set of regressions (Franconi and Stander [11]) is used for generating flexible perturbation, for the protection varies according to identifiability of the enterprise; a spatial aggregation strategy is also proposed, based on principal components analysis. The inferential usefulness of the released data and the protection achieved by the strategy are evaluated.
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Polettini, S., Franconi, L., Stander, J. (2002). Model Based Disclosure Protection. In: Domingo-Ferrer, J. (eds) Inference Control in Statistical Databases. Lecture Notes in Computer Science, vol 2316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47804-3_7
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DOI: https://doi.org/10.1007/3-540-47804-3_7
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