Home > Research > Publications & Outputs > To blank or not to blank?

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Published

Standard

To blank or not to blank? A comparison of the effects of disclosure limitation methods on nonlinear regression estimates. / Lechner, Sandra; Pohlmeier, Winfried.
International Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases . ed. / Josep Domingo-Ferrer; Vicenc Torra. Berlin: Springer-Verlag, 2004. p. 187-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3050).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Lechner, S & Pohlmeier, W 2004, To blank or not to blank? A comparison of the effects of disclosure limitation methods on nonlinear regression estimates. in J Domingo-Ferrer & V Torra (eds), International Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3050, Springer-Verlag, Berlin, pp. 187-200. https://doi.org/10.1007/978-3-540-25955-8_15

APA

Lechner, S., & Pohlmeier, W. (2004). To blank or not to blank? A comparison of the effects of disclosure limitation methods on nonlinear regression estimates. In J. Domingo-Ferrer, & V. Torra (Eds.), International Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases (pp. 187-200). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3050). Springer-Verlag. https://doi.org/10.1007/978-3-540-25955-8_15

Vancouver

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, editors, International Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases . Berlin: Springer-Verlag. 2004. p. 187-200. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-25955-8_15

Author

Lechner, Sandra ; Pohlmeier, Winfried. / To blank or not to blank? A comparison of the effects of disclosure limitation methods on nonlinear regression estimates. International Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases . editor / Josep Domingo-Ferrer ; Vicenc Torra. Berlin : Springer-Verlag, 2004. pp. 187-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inbook{cd012c530efc41d9b9c2bf1cc5ab389e,
title = "To blank or not to blank?: A comparison of the effects of disclosure limitation methods on nonlinear regression estimates",
abstract = "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.",
keywords = "Blanking, Disclosure limitation, Errors in variables in nonlinear models, Semi-parametric selection models",
author = "Sandra Lechner and Winfried Pohlmeier",
year = "2004",
month = dec,
day = "31",
doi = "10.1007/978-3-540-25955-8_15",
language = "English",
isbn = "3540221182",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "187--200",
editor = "Josep Domingo-Ferrer and Vicenc Torra",
booktitle = "International Workshop on Privacy in Statistical Databases",

}

RIS

TY - CHAP

T1 - To blank or not to blank?

T2 - A comparison of the effects of disclosure limitation methods on nonlinear regression estimates

AU - Lechner, Sandra

AU - Pohlmeier, Winfried

PY - 2004/12/31

Y1 - 2004/12/31

N2 - 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.

AB - 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.

KW - Blanking

KW - Disclosure limitation

KW - Errors in variables in nonlinear models

KW - Semi-parametric selection models

U2 - 10.1007/978-3-540-25955-8_15

DO - 10.1007/978-3-540-25955-8_15

M3 - Chapter

AN - SCOPUS:35048827490

SN - 3540221182

SN - 9783540221180

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 187

EP - 200

BT - International Workshop on Privacy in Statistical Databases

A2 - Domingo-Ferrer, Josep

A2 - Torra, Vicenc

PB - Springer-Verlag

CY - Berlin

ER -