An evaluation of three disclosure limitation models

Abstract

In recent years, the random noise method has been gaining wider use in National Statistical Institutes (NSIs) to protect respondent data from disclosure. The random noise method takes a micro-data approach to disclosure limitation: a multiplier (noise factor) is applied to each unit prior to tabulation – thus, guaranteeing that different tabulations, from the lowest to the highest level, are consistent. In this paper we evaluate two different random noise models in an applied context. Our analysis suggests that both the Basic Noise Model (BNM) and the Alternate Noise Model (ANM) are unsatisfactory for protecting smaller units. To overcome this difficulty, we developed a(third) Mixed Perturbation Model (MPM) that combines the use of multiplicative noise to protect large units with the use of synthetic models to protect the smaller units. To accomplish this, we constructed a hybrid model (first logistic and then linear) to generate the synthetic data. Results indicate that the mixed approach performs better than either of the other two models, both in terms of reliability and disclosure limitation, although it too has weaknesses. Hence, areas for future study remain that our research suggests might be tackled next.