Abstract
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 [4] and the Inverse Probability Weighting (IPW) approach going back to [8]. We produce Monte-Carlo evidence on how the reduction of data quality can be minimized by this masking procedure.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brand, R.: Anonymität Von Betriebsdaten. Beiträge zur Arbeitsmarkt- und Berufsforschung, BeitrAB 237, IAB, Nürnberg (2000)
Carroll, R.J., Kuechenhoff, H., Lombard, F., Stefanski, L.A.: Asymptotics for the Simex Estimator in Structural Measurement Error Models. Journal of the American Statistical Association 91, 242–250 (1996)
Carroll, R.J., Ruppert, D., Stefanski, L.A.: Measurement Error in Nonlinear Models. Chapman and Hall, Boca Raton (1995)
Cook, J.R., Stefanski, L.A.: A Simulation Extrapolation Method for Parametric Measurement Error Models. Journal of the American Statistical Association 89, 1314–1328 (1994)
Domingo-Ferrer, J., Torra, V.: Disclosure Control Methods and Information Loss for Microdata. In: Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 93–112. North-Holland, Amsterdam (2002)
Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman and Hall, New York (1993)
Fuller, W.A.: Measurement Error Models. Wiley, Chichester (1987)
Horvitz, D., Thompson, D.: A Generalization of Sampling Without Replacement from a Finite Population. Journal of the American Statistical Association 47, 663–685 (1952)
Lechner, S., Pohlmeier, W.: To Blank or Not to Blank? A Comparison of the Effects of Disclosure Limitation Methods on Nonlinear Regression Estimates. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 187–200. Springer, Heidelberg (2004)
Pohlmeier, W., Ronning, G., Wagner, J.: Econometrics of Anonymized Micro Data. Sonderband der Jahrbücher für Nationalökonomie und Statistik 225 (2005)
Rubin, D.B.: Inference and Missing Data. Biometrika 63, 581–592 (1976)
Willenborg, L., de Waal, T.: Statistical Disclosure Control in Practice. Lecture Notes in Statistics, vol. 155. Springer, Berlin (2000)
Willenborg, L., de Waal, T.: Elements of Statistical Disclosure Control. Lecture Notes in Statistics, vol. 111. Springer, Heidelberg (1996)
Wooldridge, J.M.: Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge (2002a)
Wooldridge, J.M.: Inverse Probability Weighted M-Estimators for Sample Selection, Attrition and Stratification. Portuguese Economic Journal 1, 117–139 (2002b)
Wooldridge, J.M.: Inverse Probability Weighted Estimation for General Missing Data Problems. Working paper, Department of Economics, Michigan State University (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Flossmann, A., Lechner, S. (2006). Combining Blanking and Noise Addition as a Data Disclosure Limitation Method. In: Domingo-Ferrer, J., Franconi, L. (eds) Privacy in Statistical Databases. PSD 2006. Lecture Notes in Computer Science, vol 4302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11930242_14
Download citation
DOI: https://doi.org/10.1007/11930242_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49330-3
Online ISBN: 978-3-540-49332-7
eBook Packages: Computer ScienceComputer Science (R0)