Make Assurance Double Sure: Combination of Two Disclosure Limitation Methods and Estimation of General Regression Models

Abstract

In order to guarantee confidentiality and privacy of firm-level data, statistical offices apply various disclosure limitation techniques. However, each anonymization technique has its protection limits, such that the probability of disclosing the individual information for some observations is not minimized. To overcome this problem, we propose to combine two separate disclosure limitation techniques blanking and multiplication of independent noise in order to protect the original dataset. 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 with the multiplicative measurement error method, and how to estimate the model by the combination of the multiplicative Simulation-Extrapolation (M-SIMEX) approach applied by Nolte (2007) and the Inverse Probability Weighting (IPW) approach going back to Horwitz and Thompson (1952). Based on Monte-Carlo simulations, we show that multiplicative measurement error combined with blanking as a masking procedure leads not necessarily to a severe reduction of the estimation quality, provided that its effects on the data generating process are known.

Keywords: Disclosure limitation technique, Multiplicative measurement error, Blanking, Simulation-Extrapolation, Inverse Probability Weighting

JEL Classification: C21, J24, J31