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To blank or not to blank?: A comparison of the effects of disclosure limitation methods on nonlinear regression estimates

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Publication date31/12/2004
Host publicationInternational Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases
EditorsJosep Domingo-Ferrer, Vicenc Torra
Place of PublicationBerlin
Number of pages14
ISBN (Electronic)9783540259558
ISBN (Print)3540221182, 9783540221180
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Statistical disclosure limitation is widely used by data collecting institutions to provide safe individual data. However, the choice of the disclosure limitation method severely affects the quality of the data and limit their use for empirical research. In particular, estimators for nonlinear models based on data which are masked by standard disclosure limitation techniques such as blanking or noise addition lead to inconsistent parameter estimates. This paper investigates to what extent appropriate econometric techniques can obtain parameter estimates of the true data generating process, if the data are masked by noise addition or blanking. Comparing three different estimators - calibration method, the SIMEX method and a semiparametric sample selectivity estimator - we produce Monte-Carlo evidence on how the reduction of data quality can be minimized by masking.