The Business Dynamics Statistics is a product of the U.S. Census Bureau that provides measures of business openings and closings, and job creation and destruction, by a variety of cross-classifications (firm and establishment age and size, industrial sector, and geography). Sensitive data are currently protected through suppression. However, as additional tabulations are being developed, at ever more detailed geographic levels, the number of suppressions increases dramatically. This paper explores the option of providing public-use data that are analytically valid and without suppressions, by leveraging synthetic data to replace observations in sensitive cells.
%0 Book Section
%1 psd2014b
%A Miranda, Javier
%A Vilhuber, Lars
%B Privacy in Statistical Databases
%D 2014
%E Domingo-Ferrer, Josep
%I Springer International Publishing
%K confidentiality data; disclosure flows; gross job labor limitation; local markets; protection statistical synthetic time-series;
%P 232-242
%R 10.1007/978-3-319-11257-2_18
%T Using Partially Synthetic Data to Replace Suppression in the Business Dynamics Statistics: Early Results
%U http://dx.doi.org/10.1007/978-3-319-11257-2_18
%V 8744
%X The Business Dynamics Statistics is a product of the U.S. Census Bureau that provides measures of business openings and closings, and job creation and destruction, by a variety of cross-classifications (firm and establishment age and size, industrial sector, and geography). Sensitive data are currently protected through suppression. However, as additional tabulations are being developed, at ever more detailed geographic levels, the number of suppressions increases dramatically. This paper explores the option of providing public-use data that are analytically valid and without suppressions, by leveraging synthetic data to replace observations in sensitive cells.
%@ 978-3-319-11256-5
@incollection{psd2014b,
abstract = {The Business Dynamics Statistics is a product of the U.S. Census Bureau that provides measures of business openings and closings, and job creation and destruction, by a variety of cross-classifications (firm and establishment age and size, industrial sector, and geography). Sensitive data are currently protected through suppression. However, as additional tabulations are being developed, at ever more detailed geographic levels, the number of suppressions increases dramatically. This paper explores the option of providing public-use data that are analytically valid and without suppressions, by leveraging synthetic data to replace observations in sensitive cells.},
added-at = {2016-09-30T21:18:21.000+0200},
author = {Miranda, Javier and Vilhuber, Lars},
biburl = {https://www.bibsonomy.org/bibtex/27cacfd115732b4bb68210de78e29decc/ncrn-cornell},
booktitle = {Privacy in Statistical Databases},
doi = {10.1007/978-3-319-11257-2_18},
editor = {Domingo-Ferrer, Josep},
interhash = {2d67787185012a946cc15cd32aa07ac5},
intrahash = {7cacfd115732b4bb68210de78e29decc},
isbn = {978-3-319-11256-5},
keywords = {confidentiality data; disclosure flows; gross job labor limitation; local markets; protection statistical synthetic time-series;},
language = {English},
pages = {232-242},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
timestamp = {2016-09-30T21:18:21.000+0200},
title = {Using Partially Synthetic Data to Replace Suppression in the Business Dynamics Statistics: Early Results},
url = {http://dx.doi.org/10.1007/978-3-319-11257-2_18},
volume = 8744,
year = 2014
}