Statistical Disclosure Control Methods for Microdata from the Labour Force Survey

Michał Pietrzak

doi:10.18778/0208-6018.348.01

Authors

Michał Pietrzak Poznań University of Economics and Business, Institute of Informatics and Quantitative Economics Department of Statistics; Statistical Office in Poznań https://orcid.org/0000-0001-8381-7881

DOI:

https://doi.org/10.18778/0208-6018.348.01

Keywords:

Statistical Disclosure Control, perturbative methods, PRAM, Additive Noise, Rank Swapping, microdata, Labour Force Survey, sdcMicro package

Abstract

The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey. In the first step, the author assessed to what extent the confidentiality of information was protected in the original dataset. In the second step, after applying selected methods implemented in the sdcMicro package in the R programme, the impact of those methods on the disclosure risk, the loss of information and the quality of estimation of population quantities was assessed. The conclusion highlights some problematic aspects of the use of Statistical Disclosure Control methods which were observed during the conducted analysis.

Downloads

Download data is not yet available.

References

Benschop T., Machingauta C., Welch M. (2019), Statistical Disclosure Control: A Practice Guide, https://readthedocs.org/projects/sdcpractice/downloads/pdf/latest/ (accessed: 13.03.2020).

Biemer P. P., Leeuw E. de, Eckman S., Edwards B., Kreuter F., Lyberg L. E., Tucker N. C., West B. T. (2017), Total Survey Error in Practice, “Wiley Series in Survey Methodology”, Wiley, New Jersey.

CSO (2012), Labour Force Survey in Poland. IV quarter 2011, Statistical Information and Elaborations, Statistical Publishing Establishment, Warsaw, https://stat.gov.pl/cps/rde/xbcr/gus/pw_aktyw_ekonom_ludn_IVkw_2011.pdf (accessed: 13.03.2020).

Domingo‑Ferrer J., Torra V. (2003), On the connections between statistical disclosure control for microdata and some artificial intelligence tools, “Information Sciences”, no. 151, pp. 153–170.

Domingo‑Ferrer J., Torra V. (2004), Disclosure risk assessment in statistical data protection, “Journal of Computational and Applied Mathematics”, no. 164–165, pp. 285–293.

Duncan G. T., Elliot M., Salazar‑González J.‑J. (2011), Statistical Confidentiality. Principles and Practice, “Statistics for Social and Behavioral Sciences”, Springer Science+Business Media, New York–Dordrecht–Heidelberg–London.

Eurostat (2019), EU Labour Force Survey Database User Guide, European Commission, https://ec.europa.eu/eurostat/documents/1978984/6037342/EULFS-Database-UserGuide.pdf (accessed: 13.03.2020).

Hundepool A., Domingo‑Ferrer J., Franconi L., Giessing S., Lenz R., Naylor J., Schulte Nordholt E., Seri G., Wolf P.‑P. de (2010), Handbook on Statistical Disclosure Control, ESSNet SDC A Network of Excellence in the European Statistical System in the field of Statistical Disclosure Control, https://ec.europa.eu/eurostat/cros/system/files/SDC_Handbook.pdf (accessed: 13.03.2020).

Hundepool A., Domingo‑Ferrer J., Franconi L., Giessing S., Schulte Nordholt E., Spicer K., Wolf P.‑P. de (2012), Statistical Disclosure Control, “Wiley Series in Survey Methodology”, Wiley, Chichester.

Lewis T. H. (2016), Complex survey data analysis with SAS, CRC Press, Taylor & Francis Group, Boca Raton.

Lohr S. L. (2010), Sampling: Design and Analysis, Second Edition, Brooks/Cole Cengage Learning, Boston.

Matthews G. J., Harel O. (2011), Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy, “Statistics Surveys”, vol. 5, pp. 1–29, http://dx.doi.org/10.1214/11-SS074

Shlomo N. (2010), Releasing Microdata: Disclosure Risk Estimation, Data Masking and Assessing Utility, “Journal of Privacy and Confidentiality”, vol. 2(1), pp. 73–91, https://journalprivacyconfidentiality.org/index.php/jpc/article/view/584/567 (accessed: 13.03.2020).

Templ M. (2017), Statistical Disclosure Control for Microdata. Methods and Applications in R, Springer, http://dx.doi.org/10.1007/978-3-319-50272-4

Templ M., Kowarik A., Meindl B. (2015), Statistical Disclosure Control for Micro‑Data Using the R Package sdcMicro, “Journal of Statistical Software”, vol. 67(4), pp. 1–36, http://dx.doi.org/10.18637/jss.v067.i04

Willenborg L., Waal T. de (2001), Elements of Statistical Disclosure Control, Springer Science+ Business Media, New York.

Wolter K. M. (2007), Introduction to Variance Estimation, Second Edition, “Statistics for Social and Behavioral Sciences”, Springer Science+Business Media, New York.