Comparison of Statistical Disclosure Control Methods: Multiple Imputation Versus Noise Multiplication.

摘要

When statistical agencies release microdata to the public, a major concern is the control of disclosure risk, while ensuring utility in the released data. Often some statistical disclosure control methods such as data swapping, multiple imputation, top coding, and perturbation with random noise, are applied before releasing the data. This article provides a comprehensive comparison of two methods, namely, multiple imputation and noise multiplication, for drawing inference about some useful parameters under the exponential, normal and log-normal models. The comparison is provided under two scenarios: (1) the entire data set is replaced by multiply imputed or noise multiplied data, and (2) only the top part of the data is similarly replaced. The latter scenario arises, for example, when top coding is used for disclosure control, especially with income data. Methodology is developed for the analysis of noise multiplied data under both scenarios. Under the situation where only the large values in the dataset are noise multiplied, data analysis methods are developed and compared under two types of data releases: (i) each released value includes an indicator of whether or not it has been noise perturbed, and (ii) no such indicator is provided.