Statistical Disclosure Control Methods for Microdata from the Labour Force Survey

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.

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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).
Google Scholar

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.
Google Scholar

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).
Google Scholar

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.
Google Scholar

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.
Google Scholar

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.
Google Scholar

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).
Google Scholar

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).
Google Scholar

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.
Google Scholar

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

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

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
Google Scholar

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).
Google Scholar

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
Google Scholar

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
Google Scholar

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

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

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Published

2020-06-22

How to Cite

Pietrzak, M. (2020). Statistical Disclosure Control Methods for Microdata from the Labour Force Survey. Acta Universitatis Lodziensis. Folia Oeconomica, 3(348), 7–24. https://doi.org/10.18778/0208-6018.348.01

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