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Prospects for Protecting Business Microdata when Releasing Population Totals via a Remote Server Cover

Prospects for Protecting Business Microdata when Releasing Population Totals via a Remote Server

Open Access
|Jun 2019

References

  1. Abrahams, C. and K. Mahony. 2008. “2New Policy and Procedures Governing the Release of Microdata Derived from ONS Social Surveys.” 13th GSS Methodology Conference, London, June 23, 2008. Available at: https://www.ons.gov.uk/ons/media-centre/events/past-events/thirteenth-gss-methodology-conference–23-june-2008 (accessed January 2018).
  2. Chipperfield, J.O. 2014. “Disclosure-Protected Inference with Linked Micro-data using a Remote Analysis Server.” Journal of Official Statistics 30: 123–146. Doi: http://dx.doi.org/10.2478/jos-2014-0007.10.2478/jos-2014-0007
  3. Chipperfield, J.O. and C. O’Keefe. 2014. “Disclosure-Protected Inference using Generalised Linear Models.” International Statistical Review 82: 371–391. Doi: https://doi.org/10.1111/insr.12054.10.1111/insr.12054
  4. Chipperfield, J.O., D. Gow, and B. Loong. 2016. “The Australian Bureau of Statistics and releasing frequency tables via a remote server.” Statistical Journal of the IAOS 1: 53–64. Doi: https://doi.org/10.3233/SJI-160969.10.3233/SJI-160969
  5. Domingo-Ferrer, J. and V. Torra. 2001. “Disclosure Protection Methods and Information Loss for Microdata.” In Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, edited by P. Doyle, J.I. Lane, J.J.M. Theeuwes, and L. Zayatz, 91–110. Amsterdam: North-Holland.
  6. Dwork, C., F. McSherry, K. Nissim, and A. Smith. 2006. “Calibrating Noise to Sensitivity in Private Data Analysis.” In Theory of Cryptography TCC, edited by S. Halevi and R. Rabin, 265–284. Heidelberg: Springer.10.1007/11681878_14
  7. Evans, T., L. Zayatz, and J. Slanta. 1998. “Using Noise for Disclosure Limitation of Establishment Tabular Data.” Journal of Official Statistics 4: 537–551. Available at: https://www.scb.se/contentassets/f6bcee6f397c4fd68db6452fc9643e68/using-noisefor-disclosure-limitation-of-establishment-tabular-data.pdf (accessed January 2019).
  8. González, J.J.S. 2005. “A Unified Mathematical Programming Framework for different Statistical Disclosure Limitation Methods.” Operations Research 53: 819–829. Doi: https://doi.org/10.1287/opre.1040.0202.10.1287/opre.1040.0202
  9. Krsinich, F. and A. Piesse. 2002. “Multiplicative Microdata Noise for Confidentialising Tables of Business Data.” Statistics New Zealand. Available at: http://archive.stats.govt.nz/browse_for_stats/businesses/business_characteristics/multiplicative-microdatanoise-for-business-data.aspx (accessed January 2019).
  10. Lucero, J., L. Zayatz, L. Singh, J. You, M. DePersio, and M. Freiman. 2011. “The Current Stage of the Microdata Analysis System at the U.S. Census Bureau.” Proceedings of the World Congress of the International Statistical Institute, 3115–3133. Dublin. Available at: http://2011.isiproceedings.org/papers/650103.pdf (accessed January 2019).
  11. Miranda, J. and L. Vilhuber. 2013. “Looking back on three years of Synthetic LBD Beta.” Cornell University. Available at: http://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article=1013&context=ldi (accessed January 2019).10.2139/ssrn.2423357
  12. O’Keefe, C. and J. Chipperfield. 2013. “A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems.” International Statistical Review 81: 426–455. Doi: https://doi.org/10.1111/insr.12021.10.1111/insr.12021
  13. Reuter, W.H. and J.M. Museux. 2010. “Establishing an Infrastructure for Remote Access to Microdata at Eurostat.” In Privacy in Statistical Databases, edited by J. Domingo-Ferrer and E. Magkos, 249–257. Berlin, Heidelberg: Springer.10.1007/978-3-642-15838-4_22
  14. Tambay, J. 2017. “A layered perturbation method for the protection of tabular outputs.” Survey Methodology 43: 31–40. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2017001/article/14818-eng.pdf?st=qzA3QL0u (accessed January 2019).
  15. Tambay, J.-L. and J.M. Fillion. 2013. “Strategies for processing tabular data using the G-Confid cell suppression software.” Proceedings of the Survey Research Methods Section. American Statistical Association Joint Statistical Meetings, Montreal, August 3–8, 2013. Available at: https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2017/7_gconfid.pdf (accessed January 2019).
  16. Thompson, G., S. Broadfoot, and D. Elazar. 2013. “Methodology for the Automatic Confdentialisation of Statistical Outputs from Remote Servers at the Australian Bureau of Statistics.” UNECE Work Session on Statistical Data Confidentiality, Ottawa, October. Available at: https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf (accessed January 2019).
  17. Yancey, W.E., W.E. Winkler, and R.H. Creecy. 2002. “Disclosure Risk Assessment in Perturbative Micro-data Protection.” In Inference Control in Statistical Databases, edited by J. Domingo-Ferrer, 135–151. New York: Springer.10.1007/3-540-47804-3_11
Language: English
Page range: 319 - 336
Submitted on: Sep 1, 2016
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Accepted on: Jan 1, 2019
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Published on: Jun 18, 2019
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 James Chipperfield, John Newman, Gwenda Thompson, Yue Ma, Yan-Xia Lin, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.