Statistical disclosure limitation is widely used by data collecting institutions to provide safe individual data. In this paper, we propose to combine two separate disclosure limitation techniques blanking and addition of independent noise in order to protect the original data. The proposed approach yields a decrease in the probability of reidentifying/disclosing the individual information, and can be applied to linear as well as nonlinear regression models.
We show how to combine the blanking method and the measurement error method, and how to estimate the model by the combination of the Simulation-Extrapolation (SIMEX) approach proposed by  and the Inverse Probability Weighting (IPW) approach going back to . We produce Monte-Carlo evidence on how the reduction of data quality can be minimized by this masking procedure.