Administrative Records for Survey Methodology. Группа авторов

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(490): 564–577. https://doi.org/10.1198/jasa.2009.ap08629.

      33 Hyatt, H., McEntarfer, E., McKinney, K., et al. (2014). JOB-TO-JOB (J2J) flows: new labor market statistics from linked employer-employee data. Working Papers 14–34. Center for Economic Studies. U.S. Census Bureau. https://ideas.repec.org/p/cen/wpaper/14-34.html.

      34 Institute for Employment Research (2016). Job submission application (JoSuA) at the Research Data Centre of the Federal Employment Agency: user manual. https://josua.iab.de/gui/manual.pdf (accessed 05 August 2020).

      35 Karp, P. (2016). Census controversy shows ABS ‘needs to do better’, says Statistical Society. The Guardian (9 August 2016). http://www.theguardian.com/australia-news/2016/aug/09/census-controversy-shows-abs-needs-to-do-better-says-statistical-society.

      36 Karr, A.F., Lin, X., Sanil, A.P., and Reiter, J.P. (2005). Secure regression on distributed databases. Journal of Computational and Graphical Statistics 14 (2): 263–279. https://doi.org/10.1198/106186005X47714.

      37 Karr, A.F., Lin, X., Sanil, A.P., and Reiter, J.P. (2006). Secure statistical analysis of distributed databases. In: Statistical Methods in Counterterrorism (eds. A.G. Wilson, G.D. Wilson and D.H. Olwell), 237–261. New York: Springer. http://link.springer.com/chapter/10.1007/0-387-35209-0_14.

      38 Karr, A.F., Lin, X., Sanil, A.P., and Reiter, J.P. (2009). Privacy-preserving analysis of vertically partitioned data using secure matrix products. Journal of Official Statistics 25 (1): 125–138.

      39 Kraus, R. (2013). Statistical Déjà vu: the national data center proposal of 1965 and its descendants. Journal of Privacy and Confidentiality 5 (1) : 1–37. https://doi.org/10.29012/jpc.v5i1.624 Accessed online (01/19/2021) at https://journalprivacyconfidentiality.org/index.php/jpc/article/view/624.

      40 Machanavajjhala, A., Kifer, D., Abowd, J.M. et al. (2008). Privacy: theory meets practice on the map. In: Proceedings of the International Conference on Data Engineering IEE, 277–286. https://doi.org/10.1109/ICDE.2008.4497436.

      41 Massell, P.B. and Funk, J.M. (2007). Recent developments in the use of noise for protecting magnitude data tables: balancing to improve data quality and rounding that preserves protection. Proceedings of the 2007 FCSM Research Conference. Council of Professional Associations on Federal Statistics. Washington, DC, USA (5–7 November, 2007). https://nces.ed.gov/FCSM/pdf/2007FCSM_Massell-IX-B.pdf

      42 Massell, P., Zayatz, L., and Funk, J. (2006). Protecting the confidentiality of survey tabular data by adding noise to the underlying microdata: application to the commodity flow survey. In: Privacy in Statistical Databases, Lecture Notes in Computer Science (eds. J. Domingo-Ferrer and L. Franconi), 304–317. Springer Berlin Heidelberg. https://doi.org/10.1007/11930242_26.

      43 McKinney, K.L. and Vilhuber, L. (2011a). LEHD data documentation LEHD-Overview-S2008-rev1. Working Papers 11–43. Center for Economic Studies. U.S. Census Bureau. https://ideas.repec.org/p/cen/wpaper/11-43.html.

      44 McKinney, K.L. and Vilhuber, L. (2011b). LEHD infrastructure files in the Census RDC: overview of S2004 snapshot. Working Papers 11–13. Center for Economic Studies. U.S. Census Bureau. https://ideas.repec.org/p/cen/wpaper/11-13.html.

      45 National Institute on Aging and the National Institutes of Health (2017). Growing Older in America: The Health and Retirement Study. University of Michigan. http://hrsonline.isr.umich.edu/index.php?p=dbook.

      46 O’Keefe, C.M., Westcott, M., Ickowicz, A., et al. (2013). Protecting confidentiality in statistical analysis outputs from a virtual data centre. Joint UNECE/Eurostat work session on statistical data confidentiality.

      47 Raab, G.M., Dibben, C., and Burton, P. (2015). Running an analysis of combined data when the individual records cannot be combined: practical issues in secure computation. Joint UNECE/Eurostat work session on statistical data confidentiality. http://www1.unece.org/stat/platform/display/SDCWS15/Statistical+Data+Confidentiality+Work+Session+Oct+2015+Home.

      48 Reiter, J.P. (2003). Model diagnostics for remote-access regression servers. Statistics and Computing 13: 371–380.

      49 Reiter, J.P. (2004). Simultaneous use of multiple imputation for missing data and disclosure limitation. Survey Methodology 30: 235–242.

      50 Reiter, J.P., Oganian, A., and Karr, A.F. (2009). Verification servers: enabling analysts to assess the quality of inferences from public use data. Computational Statistics & Data Analysis 53 (4): 1475–1482. https://doi.org/10.1016/j.csda.2008.10.006.

      51 Rubin, D.B. (1987). The calculation of posterior distributions by data augmentation: comment: a noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: the SIR algorithm. Journal of the American Statistical Association 82 (398): 543–546.

      52 Sanil, A.P., Karr, A.F., Lin, X., and Reiter, J.P. (2004). Privacy preserving regression modelling via distributed computation. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 677–682. ACM. https://doi.org/10.1145/1014052.1014139.

      53 Schiller, D. and Welpton, R. (2014). Distributing access to data, not data – providing remote access to European microdata. IASSIST Quarterly 38 (3). https://www.iassistquarterly.com/index.php/iassist/article/view/122.

      54 Schouten, B. and Cigrang, M. (2003). Remote access systems for statistical analysis of microdata. Discussion Paper 03004. Statistics Netherlands. https://www.oecd.org/std/37502934.pdf.

      55 Sonnega, A. and Weir, D.R. (2014). The Health and Retirement Study: a public data resource for research on aging. Open Health Data 2 (1): 576. https://doi.org/10.5334/ohd.am.

      56 Torra, V., Abowd, J.M., and Domingo-Ferrer, J. (2006). Using Mahalanobis distance-based record linkage for disclosure risk assessment. In: Privacy in Statistical Databases, vol. 4302 (eds. J. Domingo-Ferrer and L. Franconi), 233–242. Berlin, Heidelberg: Springer Berlin Heidelberg. http://link.springer.com/10.1007/11930242_20.

      57 U.S.

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