Standard
Combining blanking and noise addition as a data disclosure limitation method. / Flossmann, Anton
; Lechner, Sandra.
Privacy in Statistical Databases - CENEX-SDC Project International Conference, PSD 2006, Proceedings. ed. / Josep Domingo-Ferrer; Luisa Franconi. Springer-Verlag, 2006. p. 152-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4302).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Flossmann, A
& Lechner, S 2006,
Combining blanking and noise addition as a data disclosure limitation method. in J Domingo-Ferrer & L Franconi (eds),
Privacy in Statistical Databases - CENEX-SDC Project International Conference, PSD 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4302, Springer-Verlag, pp. 152-163, CENEX-SDC Project of International Conference on Privacy in Statistical Databases, PSD2006, Rome, Italy,
13/12/06.
https://doi.org/10.1007/11930242_14APA
Vancouver
Flossmann A
, Lechner S.
Combining blanking and noise addition as a data disclosure limitation method. In Domingo-Ferrer J, Franconi L, editors, Privacy in Statistical Databases - CENEX-SDC Project International Conference, PSD 2006, Proceedings. Springer-Verlag. 2006. p. 152-163. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11930242_14
Author
Bibtex
@inproceedings{2b6156e8a31b4734b994aa6600e418b5,
title = "Combining blanking and noise addition as a data disclosure limitation method",
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 proba bility 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.",
keywords = "Blanking, Disclosure limitation technique, Error-in-variables, IPW, SIMEX",
author = "Anton Flossmann and Sandra Lechner",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.; CENEX-SDC Project of International Conference on Privacy in Statistical Databases, PSD2006 ; Conference date: 13-12-2006 Through 15-12-2006",
year = "2006",
doi = "10.1007/11930242_14",
language = "English",
isbn = "9783540493303",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "152--163",
editor = "Josep Domingo-Ferrer and Luisa Franconi",
booktitle = "Privacy in Statistical Databases - CENEX-SDC Project International Conference, PSD 2006, Proceedings",
}
RIS
TY - GEN
T1 - Combining blanking and noise addition as a data disclosure limitation method
AU - Flossmann, Anton
AU - Lechner, Sandra
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2006.
PY - 2006
Y1 - 2006
N2 - 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 proba bility 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.
AB - 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 proba bility 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.
KW - Blanking
KW - Disclosure limitation technique
KW - Error-in-variables
KW - IPW
KW - SIMEX
UR - http://www.scopus.com/inward/record.url?scp=67349207199&partnerID=8YFLogxK
U2 - 10.1007/11930242_14
DO - 10.1007/11930242_14
M3 - Conference contribution/Paper
AN - SCOPUS:67349207199
SN - 9783540493303
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 152
EP - 163
BT - Privacy in Statistical Databases - CENEX-SDC Project International Conference, PSD 2006, Proceedings
A2 - Domingo-Ferrer, Josep
A2 - Franconi, Luisa
PB - Springer-Verlag
T2 - CENEX-SDC Project of International Conference on Privacy in Statistical Databases, PSD2006
Y2 - 13 December 2006 through 15 December 2006
ER -
