NDLI: Microdata disclosure by resampling – Empirical findings for business survey data

Content Provider	Springer Nature Link
Author	Gottschalk, Sandra
Copyright Year	2004
Abstract	One specific problem statistical offices and research institutes are faced with when releasing microdata is the preservation of confidentiality. Traditional methods to avoid disclosure often destroy the structure of the data, and information loss is potentially high. In this paper an alternative technique of creating scientific–use files is discussed, which reproduces the characteristics of the original data quite well. It is based on Fienberg (1997, 1994) who estimates and resamples from the empirical multivariate cumulative distribution function of the data in order to get synthetic data. The procedure creates data sets – the resample – which have the same characteristics as the original survey data. The paper includes some applications of this method with (a) simulated data and (b) innovation survey data, the Mannheim Innovation Panel (MIP), and a comparison between resampling and a common method of disclosure control (disturbance with multiplicative error) with regard to confidentiality on the one hand and the appropriateness of the disturbed data for different kinds of analyses on the other. The results show that univariate distributions can be better reproduced by unweighted resampling. Parameter estimates can be reproduced quite well if the resampling procedure implements the correlation structure of the original data as a scale or if the data is multiplicatively perturbed and a correction term is used. On average, anonymization of data with multiplicatively perturbed values protects better against re–identification than the various resampling methods used.
Ending Page	302
Page Count	24
Starting Page	279
File Format	PDF
ISSN	00026018
e-ISSN	16140176
Journal	AStA Advances in Statistical Analysis
Issue Number	3
Volume Number	88
Language	English
Publisher	Physica-Verlag
Publisher Date	2004-01-01
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access Estimation: General Monte Carlo studies Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Statistical Simulation Methods: General business survey data Resampling Econometrics Statistics multiplicative data perturbation
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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