Abstract
In official statistics, it is a serious problem to be able to estimate the original data from the numerical values of result tables. To limit such problems, cell suppression is frequently applied when creating result tables, and when researchers create summary tables that include levels of descriptive statistics for remote access, statistical disclosure control must be applied. This research therefore focuses on higher-order moments in descriptive statistics to perform empirical analysis of the safety of statistical levels. The results from standard deviation (variance), skewness, and kurtosis confirm that cells with frequency of 10 or higher are unsafe.
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References
Brandt, M., Franconi, L., Guerke, C., Hundepool, A., Lucarelli, M., Mol, J., Ritchie, F., Seri, G., Welpton, R.: Guidelines for the checking of output based on microdata research. Final Report of ESSnet Sub-Group on Output SDC, Eurostat (2010)
Bring, J., Wang, Q.: Comparison of different sensitivity rules for tabular data and presenting a new rule – the Interval Rule. In: Domingo-Ferrer, J. (ed.) PSD 2014. LNCS, vol. 8744, pp. 36–47. Springer, Heidelberg (2014)
De Kort, S., Wathan, J.: Guide to Imputation and Perturbation in the Samples of Anonymised Records (2009) (unpublished)
Desai, T., Ritchie, F., Welpton, R.: Five Safes: Designing Data Access for Research. Economics Working Paper Series 1601, University of the West of England (2016)
Domingo-Ferrer, J., Torra, V.: Disclosure control methods and information loss for microdata. In: Doyle, P., et al. (eds.) Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 91–110. Elsevier Science, Amsterdam (2001)
Duncan, G.T., Elliot, M., Salazar-González, J.: Statistical Confidentiality. Springer, New York (2011)
Giessing, S.: Survey on methods for tabular data protection in ARGUS. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 1–13. Springer, Heidelberg (2004)
Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Nordholt, E.S., Spicer, K., de Wolf, P.P.: Statistical Disclosure Control. Wiley, United Kingdom (2012)
Ito, S., Hoshino, N.: Assessing the effectiveness of disclosure limitation methods for census microdata in Japan. Paper presented at Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality, Ottawa, Canada, pp. 1–10 (2013)
Ito, S., Hoshino, N.: Data swapping as a more efficient tool to create anonymized census microdata in Japan. Paper presented at Privacy in Statistical Databases 2014, Ibiza, Spain, pp. 1–14 (2014)
Loeve, A.: Note on Sensitivity Measures and Protection Levels. Technical report, Research Paper, Statistics Netherlands (2001)
Ritchie, F.: Secure access to confidential microdata: four years of the virtual microdata laboratory. Econ. Labour Mark. Rev. 2(5), 29–34 (2008)
Ritchie, F., Welpton, R.: Operationalizing principles-based output statistical disclosure control. mimeo (2015)
Takemura, A.: Local recoding and record swapping by maximum weight matching for disclosure control of microdata sets. J. Official Stat. 18(2), 275–289 (2002)
Willenborg, L., de Waal, T.: Elements of Statistical Disclosure Control. Springer, New York (2001)
Zayatz, L.: Disclosure avoidance practices and research at the U.S. Census Bureau: an update. J. Official Stat. 23(2), 253–265 (2007)
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Shirakawa, K., Abe, Y., Ito, S. (2016). Empirical Analysis of Sensitivity Rules: Cells with Frequency Exceeding 10 that Should Be Suppressed Based on Descriptive Statistics. In: Domingo-Ferrer, J., Pejić-Bach, M. (eds) Privacy in Statistical Databases. PSD 2016. Lecture Notes in Computer Science(), vol 9867. Springer, Cham. https://doi.org/10.1007/978-3-319-45381-1_3
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DOI: https://doi.org/10.1007/978-3-319-45381-1_3
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