Abstract
When creating synthetic microdata in Japan, the values from result tables are used to remove links to individual data. The result tables of conventional official statistics do not allow the generation of random numbers for reproducing individual data. Therefore, the National Statistics Center has created pseudo-individual data on a trial basis using the 2004 National Survey of Family Income and Expenditure. Although mean, variance, and correlation coefficient in the original data were reproduced in the synthetic microdata created, the trial did not include the creation of completely synthetic microdata from the result tables, and the reproduction of the distribution was not taken into account. In this study, a method for generating random numbers with a distribution close to that of the original data was tested, and new type of synthetic microdata called an ‘Academic Use File’ was created. Random numbers were generated completely from the values contained in the result tables. In addition, this test took into account the Anscombe’s quartet as well as the sensitivity rule. As a result, based on the numerical values of the result tables, it was possible to determine the approach that best approximates the distribution type of the original data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anscombe, F.J.: Graphs in Statistical Analysis. Am. Stat. 27, 17–21 (1973)
Bethlehem, J.G., Keller, W.J., Pannekoek, J.: Disclosure Control of Microdata. J. Am. Stat. Assoc. 85(409), 38–45 (1990)
Defays, D., Anwar, M.N.: Masking Microdata Using Micro-Aggregation. J. Offic. Stat. 14(4), 449–461 (1998)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical Data-oriented Microaggregation for Statistical Disclosure Control. IEEE Trans. Knowl. Data Eng. 14(1), 189–201 (2002)
Höhne, J.: SAFE-a method for statistical disclosure limitation of microdata. In: Monographs of Official Statistics, pp. 1–3 (2003)
Hundepool, A., van de Wetering, A., Ramaswamy, R., Franconi L., Polenttini, S., Capobianchi, A., de Walf, P. P., Domingo-Ferrer, J., Torra, V., Brand, R., Giessing, S.: μ-ARGUS Version 4.2 Software and User’s Manual. Statistics Netherlands, Vooburg NL (2008). http://neon.vb.cbs.nl/casc/Software/MuManual4.2.pdf
Ito, S., Isobe, S., Akiyama, H.: A Study on Effectiveness of microaggregation as disclosure avoidance methods: based on national survey of family income and expenditure. NSTAC Working Paper 10, pp. 33–66 (2008). (in Japanese)
Ito, S.: On Microaggregation as Disclosure Avoidance Methods. J. Econ. Kumamoto Gakuen Univ. 15(3/4), 197–232 (2009) (in Japanese)
Makita, N., Ito, S., Horikawa, A., Goto, T., Yamaguchi, K.: Development of synthetic microdata for educational use in Japan. Paper Presented at 2013 Joint IASE/IAOS Satellite Conference, Macau Tower, Macau, China, pp. 1–9 (2013)
Shirakawa, K., Abe, Y., Ito, S.: Empirical analysis of sensitivity rules: cells with frequency exceeding 10 that should be suppressed based on descriptive statistics. In: Privacy in Statistical Databases, Dubrovnik, Croatia, September 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Shirakawa, K., Abe, Y., Ito, S. (2016). Creating an ‘Academic Use File’ Based on Descriptive Statistics: Synthetic Microdata from the Perspective of Distribution Type. 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_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-45381-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45380-4
Online ISBN: 978-3-319-45381-1
eBook Packages: Computer ScienceComputer Science (R0)