style="font-size:15px;"> 6 Abowd, J.M., Stephens, B.E., Vilhuber, L. et al. (2009). The LEHD infrastructure files and the creation of the quarterly workforce indicators. In: Producer Dynamics: New Evidence from Micro Data (eds. T. Dunne, J.B. Jensen and M.J. Roberts). University of Chicago Press.7 Abowd, J.M., Kaj Gittings, R., McKinney, K.L., et al. (2012). Dynamically consistent noise infusion and partially synthetic data as confidentiality protection measures for related time series. US Census Bureau Center for Economic Studies Paper No. CES-WP-12-13. http://dx.doi.org/10.2139/ssrn.2159800.
8 Abowd, J.M., Schmutte, I.M., and Vilhuber, L. (2018). Disclosure avoidance and confidentiality protection in linked data. U.S. Census Bureau Center for Economic Studies Working Paper CES-WP-18-07.
9 Australian Bureau of Statistics (2015). Media release – ABS response to privacy impact assessment. Australian Bureau of Statistics. http://abs.gov.au/AUSSTATS/[email protected]/mediareleasesbyReleaseDate/C9FBD077C2C948AECA257F1E00205BBE?OpenDocument (accessed 05 August 2020).
10 Bender, S. and Heining, J. (2011). The research-data-centre in research-data-centre approach: a first step towards decentralised international data sharing. IASSIST Quarterly/International Association for Social Science Information Service and Technology 35 (3) https://www.iassistquarterly.com/index.php/iassist/article/view/119.
11 Browning, M., Jones, S., and Kuhn, P.J. (1995). Studies of the Interaction of UI and Welfare Using the COEP Dataset. LU2-153/224-1995E, Unemployment Insurance Evaluation Series. Ottawa: Human Resources Development Canada. http://publications.gc.ca/collections/collection_2015/rhdcc-hrsdc/LU2-153-224-1995-eng.pdf.
12 Bruno, G., D’Aurizio, L., and Tartaglia-Polcini, R. (2009). Remote processing of firm microdata at the Bank of Italy. No. 36, Bank of Italy. http://dx.doi.org/10.2139/ssrn.1396224 (accessed 05 August 2020).
13 Bruno, G., D’Aurizio, L., and Tartaglia-Polcini, R. (2014). Remote processing of business microdata at the Bank of Italy. In: Statistical Methods and Applications from a Historical Perspective, Studies in Theoretical and Applied Statistics (eds. F. Crescenzi and S. Mignani), 239–249. Springer International Publishing. http://link.springer.com/chapter/10.1007/978-3-319-05552-7_21.
14 Center for Economic Studies (2016). LODES Version 7. OTM20160223. U.S. Census Bureau. http://lehd.ces.census.gov/doc/help/onthemap/OnTheMapDataOverview.pdf (accessed 05 August 2020).
15 Currie, R. and Fortin, S. (2015). Social statistics matter: history of the Canadian Research Data Center Network. Canadian Research Data Centre Network. http://rdc-cdr.ca/sites/default/files/social-statistics-matter-crdcn-history.pdf (accessed 05 August 2020).
16 Dalenius, T. and Reiss, S.P. (1982). Data-swapping: a technique for disclosure control. Journal of Statistical Planning and Inference 6 (1): 73–85. https://doi.org/10.1016/0378-3758(82)90058-1.
17 Deang, L.P. and Davies, P.S. (2009). Access restrictions and confidentiality protections in the Health and Retirement Study. No. 2009–01, U.S. Social Security Administration. https://www.ssa.gov/policy/docs/rsnotes/rsn2009-01.html.
18 DeSalvo, B., Limehouse, F.F., and Klimek, S.D. (2016). Documenting the business register and related economic business data. Working Papers 16–17. Center for Economic Studies. U.S. Census Bureau. https://ideas.repec.org/p/cen/wpaper/16-17.html.
19 Duncan, G.T., Jabine, T.B., and de Wolf, V.A. (eds.); Panel on Confidentiality and Data Access, Committee on National Statistics, Commission on Behavioral and Social Sciences and Education, National Research Council and the Social Science Research Council (1993). Private Lives and Public Policies: Confidentiality and Accessibility of Government Statistics. Washington, DC: National Academy of Sciences.
20 Duncan, G.T., Elliot, M., and Salazar-González, J.J. (2011). Statistical Confidentiality: Principles and Practice, Statistics for Social and Behavioral Sciences. New York: Springer-Verlag.
21 Dwork, C. (2006). Differential privacy. In: Automata, Languages and Programming, Lecture Notes in Computer Science, vol. 4052 (eds. M. Bugliesi, B. Preneel, V. Sassone and I. Wegener), 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg. http://link.springer.com/10.1007/11787006_1.
22 Dwork, C. and Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9 (3–4): 211–407. https://doi.org/10.1561/0400000042.
23 Dwork, C., McSherry, F., Nissim, K., Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Theory of Cryptography Conference, pp. 265–284.
24 Dwork, C., Smith, A., Steinke, T., Ullman, T. (2017). Exposed! A Survey of Attacks on Private Data. Annual Review of Statistics and Its Application, 4 (1): 61–84.
25 Evans, T., Zayatz, L., and Slanta, J. (1998). Using noise for disclosure limitation of establishment tabular data. Journal of Official Statistics 14 (4): 537–551.
26 FCSM (2005). Report on statistical disclosure limitation methodology. Working Paper 22 (second version, 2005). Federal Committee on Statistical Methodology. https://s3.amazonaws.com/sitesusa/wp-content/uploads/sites/242/2014/04/spwp22.pdf.
27 Fellegi, I.P. (1972). On the question of statistical confidentiality. Journal of the American Statistical Association 67 (337): 7–18.
28 Fellegi, I.P. and Sunter, A.B. (1969). A theory for record linkage. Journal of the American Statistical Association 64 (328): 1183–1210. https://doi.org/10.1080/01621459.1969.10501049.
29 Fienberg, S.E. (2005). Confidentiality and disclosure limitation. In: Encyclopedia of Social Measurement (ed. K. Kempf-Leonard), 463–469. New York, NY: Elsevier.
30 Gittings, R. (2009). Essays in labor economics and synthetic data methods. PhD thesis. Cornell University, Ithaca, NY, USA. https://ecommons.cornell.edu/handle/1813/14039.
31 Gittings, R.K. and Schmutte, I.M. (2016). Getting handcuffs on an octopus: minimum wages, employment, and turnover. ILR Review 69 (5): 1133–1170. https://doi.org/10.1177/0019793915623519.
32 Holan, S.H., Toth, D., Ferreira, M.A.R., and Karr, A.F. (2010). Bayesian multiscale multiple imputation with implications for data
