Читать книгу Administrative Records for Survey Methodology - Группа авторов - Страница 59

References

Оглавление

1 Abowd, J.M. and McKinney, K.L. (2016). Noise infusion as a confidentiality protection measure for graph-based statistics. Statistical Journal of the IAOS 32 (1): 127–135. https://doi.org/10.3233/SJI-160958.

2 Abowd, J.M. and Schmutte, I.M. (2015). Economic analysis and statistical disclosure limitation. Brookings Papers on Economic Activity 50 (1): 221–267.

3 Abowd, J.M. and Vilhuber, L. (2012). Did the housing price bubble clobber local labor market job and worker flows when it burst? The American Economic Review 102 (3): 589–593. https://doi.org/10.1257/aer.102.3.589.

4 Abowd, J.M., Haltiwanger, J., and Lane, J. (2004). Integrated longitudinal employer–employee data for the United States. The American Economic Review 94 (2): 224–229.

5 Abowd, J.M., Stinson, M., and Benedetto, G. (2006). Final Report to the Social Security Administration on the SIPP/SSA/IRS Public Use File Project. 1813/43929. U.S. Census Bureau. http://hdl.handle.net/1813/43929.

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/abs@.nsf/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 confidentiality. Journal of the American Statistical Association 105 (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. Census Bureau (2015). SIPP Synthetic Beta Version 6.0.2. Washington, DC and Ithaca, NY, USA. http://www2.vrdc.cornell.edu/news/data/sipp-synthetic-beta-file/ (accessed 05 August 2020).

58 U.S. Department of Labor (DOL). (1987). Standard Industrial Classification (SIC) Manual. Occupational Safety and Health Administration (OSHA): https://www.osha.gov/data/sic-manual (accessed 01/19/2021).

59 United Nations (2007). Managing Statistical Confidentiality and Microdata Access – Principles and Guidelines of Good Practice. United Nations Economic Commission for Europe – Conference of European Statisticians. https://www.unece.org/fileadmin/DAM/stats/publications/Managing.statistical.confidentiality.and.microdata.access.pdf.

60 Vilhuber, L. (2013). Methods for Protecting the Confidentiality of Firm-Level Data: Issues and Solutions. 19. Labor Dynamics Institute. http://digitalcommons.ilr.cornell.edu/ldi/19/.

61 Vilhuber, L. (2017). Labordynamicsinstitute/rampnoise: code for multiplicative noise infusion. https://doi.org/10.5281/zenodo.1116352 (accessed 05 August 2020).

62 Vilhuber, L. and McKinney, K. (2014). LEHD infrastructure files in the Census RDC – overview. Working Papers 14–26. Center for Economic Studies, U.S. Census Bureau. http://ideas.repec.org/p/cen/wpaper/14-26.html.

63 Weinberg, D.H., Abowd, J.M., Steel, P.M., et al. (2007). Access methods for United States microdata. Working Papers 07–25. Center for Economic Studies, U.S. Census Bureau. https://ideas.repec.org/p/cen/wpaper/07-25.html.

Administrative Records for Survey Methodology

Подняться наверх