References
- Abowd, J.M. 2017. “Research data centers, reproducible science, and confidentiality protection: the role of the 21st century statistical agency.” Presentation to Summer DemSem, Wisconsin Federal Statistical RDC. June 5, Madison, Wisconsin. Available at: https://www2.census.gov/cac/sac/meetings/2017-09/role-statistical-agency.pdf (accessed February 2023).
- Abowd, J.M., I.M. Schmutte, W.N. Sexton, and L. Vilhuber. 2019. “Why the economics profession cannot cede the discussion of privacy protection to computer scientists.” Presentation to The Future of Economic Research under Rising Risks and Costs of Information Disclosure. Allied Social Science Associations Annual Meetings. January 5, Atlanta, Georgia. https://ecommons.cornell.edu/handle/1813/60836 (accessed February 2023).
- Abowd., J.M. 2021a. Declaration of John M. Abowd. Case no. 3:21-CV-211-RAH-ECM-KCN. U.S. District Court for the Middle District of Alabama. Availabe at: https://censusproject.files.wordpress.com/2021/04/2021.04.13-abowd-declaration-alabama-v.-commerce-ii-final-signed.pdf (accessed June 2023).
- Abowd, J.M. 2021b. Supplemental Declaration of John M. Abowd. Case no. 3:21-CV-211-RAH-ECM-KCN. U.S. District Court for the Middle District of Alabama. Available at: https://www.brennancenter.org/sites/default/files/2021-06/M.D.%20Ala.%2021-cv-00211%20dckt%20000116_001%20filed%202021-04-26%20Abowd%20declaration.pdf (accessed June 2023).
- Abowd J.M., and M.B. Hawes. 2022. Confidentiality Protection in the 2020 US Census Population and Housing. Available at: https://arxiv.org/pdf/2206.03524.pdf (accessed February 2023).
- Adam, N.R., and J.C. Worthmann. 1989. “Security-control methods for statistical databases: a comparative study.” ACM Computing Surveys 21(4): 515–556. DOI: https://doi.org/10.1145/76894.76895.
- Alabama. 2021. Alabama v. U.S. Dep’t of Commerce. 2021. Brennan Center for Justice. https://www.brennancenter.org/our-work/court-cases/alabama-v-us-dept-commerce (accessed February 2023).
- Antal, L., T. Enderle, and S. Giessing. 2017. Statistical disclosure control methods for harmonised protection of census data. Deliverable D3.1 Part I, Eurostat contract “Harmonised Protection of Census Data in the ESS”, Available at: https://ec.europa.eu/eurostat/cros/system/files/methods_for_protectingcensus_data.pdf (accessed February 2023).
- Associated Press. 2021. “16 states back Alabama’s challenge to Census privacy tool.” U.S. News. Available at: https://www.usnews.com/news/us/articles/2021-04-13/16-states-back-alabamas-challenge-to-census-privacy-tool (accessed February 2023).
- Bach. 2022. “Differential privacy and noisy confidentiality concepts for European population statistics.” Journal of Survey Statistics and Methodology, 10: 642–687. DOI: https://doi.org/10.1093/jssam/smab044.
- Bun, M., and T. Steinke. 2016. “Concentrated differential privacy: simplifications, extensions, and lower bounds.” In. Theory of Cryptography Conference-TCC, October 31–November 3, Beijing, China. Springer: 635-658. DOI:https://doi.org/10.1007/978-3-662-53641-4_24. https://link.springer.com/chapter/10.1007/978-3-662-53641-4_24.
- Cornell. 2021. Census 2020 results: Data and Analyses for New York from the data products as they are released over time by the U.S. Census Bureau. Cornell Program on Applied Demographics. Available at: https://pad.human.cornell.edu/census2020/index.cfm#das (accessed February 2023).
- Dalenius. T. 1977. “Towards a Methodology for Statistical Disclosure Control.” Statistisk Tidskrift 15: 429–444. Available at: https://ecommons.cornell.edu/bitstream/handle/1813/111303/dalenius-1977.pdf?sequence=3&isAllowed=y (accessed June 2023).
- Daily, D. 2022. Disclosure avoidance protections for the American Community Survey. U.S. Census Bureau. Available at: https://www.census.gov/newsroom/blogs/random-samplings/2022/12/disclosure-avoidance-protections-acs.html (accessed February 2023).
- Dajani, A.N., A.D. Lauger, P.E. Singer, D. Kifer, J.P. Reiter, A. Machanavajjhala, S.L. Garfinkel, S.A. Dahl, M. Graham, V. Karwa, H. Kim, P. Leclerc, I.M. Schmutte, W.N. Sexton, L. Vilhuber, and J.M. Abowd. 2017. “The modernization of statistical disclosure limitation at the U.S. Census Bureau. In Census Scientific Advisory Committee Meeting, Sepember 14–15, Suitland MD, USA. Available at: https//www.census.gov/library/video/2017/2017-09-sac.html (accessed February 2023).
- Denning, D.E., and J. Schlorer. 1980. “A fast procedure for finding a tracker in a statistical database.” ACM Transactions on Database Systems 5(1): 88–102. DOI: https://doi.org/10.1145/320128.320138.
- Dinur, I., and K. Nissim. 2003. “Revealing information while preserving privacy.” In. Proceedings of the 22nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Jun. 9–12. San Diego CA, USA.: 202–210, DOI: 10.1145/773153.773173; https://dl.acm.org/doi/10.1145/773153.773173.
- Domingo-Ferrer, J.D. Sánchez, and A. Blanco-Justicia. 2021. “The limits of differential privacy (and its misuse in data release and machine learning).” Communications of the ACM 64(7): 33–35. DOI: https://doi.org/10.1145/3433638.
- Dove, I. 2021. Applying differential privacy protection to ONS mortality data, pilot study. Office for National Statistics. Available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/methodologies/applyingdifferential-privacyprotectiontoonsmortalitydatapilotstudy (accessed June 2023).
- Duncan, G.T., S.A. Keller-McNulty, and S. Lynne. 2001. Disclosure Risk vs. Data Utility: The RU Confidentiality Map. National Institute of Statistical Sciences. Technical Report no. 121. Available at: https://www.niss.org/sites/default/files/technicalreports/tr121.pdf (accessed February 2023).
- Dupre, S. 2020. Disclosure avoidance and the Census. Select Topics in International Censuses. U.S. Census Bureau. Available at: https://www.census.gov/content/dam/-Census/library/working-articles/2020/demo/disclosure_avoidance_and_the_census_-brief.pdf (accessed February 2023).
- Dwork, C. 2011. “A firm foundation for private data analysis.” Communications of the ACM 54(1): 86–95. DOI: https://doi.org/10.1145/1866739.1866758.
- Dwork, C.N. Kohli, and D. Mulligan. 2019. “Differential privacy in practice: expose your epsilons!” Journal of Privacy and Confidentiality 9(2): 9–22. DOI: https://doi.org/10.29012/jpc.689.
- Dwork, C., F. McSherry, K. Nissim, and A. Smith. 2006. “Calibrating noise to sensitivity in private data analysis.” In Theory of Cryptography Conference – TCC 2006, March. 4–7, New York NY USA. Springer: 265–284. DOI: https://doi.org/10.1007/11681878 https://link.springer.com/chapter/10.1007/11681878_14.
- Dwork, C. and A. Roth. 2014. The Algorithmic Foundations of Differential Privacy. Now Publishers. DOI: https://doi.org/10.1561/0400000042.
- Francis, p. 2022. “A note on the misinterpretation of the US Census re-identification attack.” In Privacy in Statistical Database – PSD 2022, Sepember 21–23, Paris, France. Springer: 299–311. DOI: https://doi.org/101007/9783-031-13945-1 https://link.springer.com/chapter/10.1007/978-3-031-13945-1_21.
- Garfinkel, S. 2019. “Deploying differential privacy for the 2020 Census of Population and Housing.” In Privacy Enhancing Technologies Symposium–PETS 2019, July 16–20, Stockholm, Sweden. Available at: https://simson.net/page/Main_Page (accessed February 2023).
- Garfinkel, S., J.M. Abowd, and C. Martindale. 2019. “Understanding database reconstruction attacks on public data.” Communications of the ACM 62(3): 46–53. DOI: https://doi.org/10.1145/3291276.3295691.
- GDPR. 2016. General Protection Regulation. Regulation (EU) 2016/679. Available at: https://gdpr-info.eu (accessed February 2023).
- Greenberg, A. 2017. “How one of Apple’s key privacy safeguards falls short.” Wired. Available at: https://www.wired.com/story/apple-differential-privacy-shortcomings/ (accessed February 2023).
- Hotz, V.J., C.R. Bollinger, T. Komarova, C.F. Manski, R.A. Moffitt, D. Nekipelov, A. Sojourner, and B.D. Spencer. 2022. “Balancing data privacy and usability in the federal statistical system.” PNAS 119(31): e2104906119. DOI: https://doi.org/10.1073/pnas.21049 06119.
- Hundepool, A.J. Domingo-Ferrer, L. Franconi, K. Spicer, P.-P. De Wolf, S. Giessing, and E. Schulte Nordholt. 2012. Statistical Disclosure Control. Wiley. DOI: https://doi.org/10.1002/9781118348239.
- Kenny, C.T.S. Kuriwaki, C. McCartan, E.T.R. Rosenman, T. Simko, and K. Imai. 2021. “The use of differential privacy for census data and its impact on redistricting: the case of the 2020 U.S. Census.” Science Advances 7(41): eabk3283. DOI: https://doi.org/10.1126/sciadv.abk3283.
- McKenna, L., and M. Haubach. 2019. Legacy techniques and current research in disclosure avoidance at the U.S. Census Bureau. Research and Methodology Directorate, U.S. Census Bureau. Available at: https://www.census.gov/library/working-articles/2019/adrm/CED-WP-2019-005.html (accessed February 2023).
- Muralidhar, K. 2022. “A re-examination of the Census Bureau reconstruction and reidentification attack. In Privacy in Statistical Database – PSD 2022, September 21–23, Paris, France. Springer: DOI: https://doi.org/10.1007/978-3-031-13945-1 https://link.springer.com/chapter/10.1007/978-3-031-13945-1_22.
- Muralidhar, K., and J. Domingo-Ferrer. 2021. “Database reconstruction is very difficult in practice!” In 2021 Joint UNECE/Eurostat Expert Meeting on Statistical Data Confidentiality, december 1–3, Poznan, Poland. Available at: https://unece.org/sites/-default/files/2021-12/SDC2021_Day1_Muralidhar_AD.pdf. (accessed February 2023).
- Muralidhar, K., and J. Domingo-Ferrer. 2022. “Census reconsiderations”. Communications of the ACM 65(6): 11. DOI: https://doi.org/10.1145/3532630.
- Muralidhar, K., and R. Sarathy. 2009. “Privacy violations in accountability data released to the public by state educational agencies.” In Federal Committee on Statistical Methodology Research Conference, November 2–4, Washington D.C. USA. Available at: https://www.researchgate.net/profile/Rathindra-Sarathy/publication/273448878_-Privacy_Violations_in_Accountability_Data_Released_to_the_Public_by_State_Educational_Agencies_Rathindra_Sarathy/links/5501d43e0cf231de076ca7b3/Privacy-Violations-in-Accountability-Data-Released-to-the-Public-by-State-Educational-Agencies-Rathindra-Sarathy.pdf (accessed June 2023).
- Percival, K. 2021. Court rejects Alabama challenge to Census plans for redistricting and privacy. Brennan Center for Justice. Available at: https://www.brennancenter.org/our-work/analysis-opinion/court-rejects-alabama-challenge-census-plans-redistricting-and-privacy (accessed February 2023).
- Ruggles. S. 2021. Personal communication, November 9.
- Ruggles, S., and D. van Riper 2022. “The role of chance in the Census Bureau database reconstruction experiment.” Population Research and Policy Review 41: 781–788. DOI: https://doi.org/10.1007/s11113-021-09674-3.
- Schneider, M. 2022. “Researchers ask Census to stop controversial privacy method.” AP News., Available at: https://apnews.com/article/census-2020-us-bureau-government-and-politics-20e683c71eeb62ee4b7792d7d8530419 (accessed February 2023).
- Sweeney, L. 2000. Simple demographics often identify people uniquely. Carnegie Mellon University, Data Privacy Working Paper 3. Available at: https://ggs685.pbworks.-com/wZfile/fetch/94376315/Latanya.pdf (accessed February 2023).
- Traub, J.F., Y. Yemini, and H. Wozniaknowski. 1984. “The statistical security of a statistical database.” ACM Transactions on Database Systems, 9(4): 672–679. DOI: https://doi.org/10.1145/1994.383392.
- UNECE-CES. 2015. (United Nations Economic Commission for Europe – Conference of European Statisticians) Recommendations for the 2020 Censuses of Population and Housing. United Nations Publications, New York, NY. Available at: https://unece.org/DAM/stats/publications/2015/ECECES41_EN.pdf (accessed February 2023).
- U.S. Census Bureau. 2021a. Legal Authority and Policies for Data Linkage at Census. Available at: https://www.census.gov/about/adrm/linkage/about/authority.html (accessed June 2023).
- U.S. Census Bureau. 2021b. Census Bureau sets key parameters to protect privacy in 2020 Census results. Release Number CB21-CN.42. Available at: https://www.census.gov/-newsroom/press-releases/2021/2020-census-key-parameters.html (accessed February 2023).
- U.S. Census Bureau. 2022. Privacy loss Budget Allocation. Available at: https://www2.census.gov/programs-surveys/decennial/2020/program-management/-data-product-planning/2010-demonstration-data-products/02-Demographic_and_Housing_Characteristics/2022-03-16_Summary_File/2022-03-16_Privacy-Loss_Budget_Allocations.pdf (accessed February 2023).
- Van Ripper, D., T. Kugler, and S. Ruggles. 2020. “Disclosure avoidance in the Census Bureau’s 2010 demonstration data product.” In Privacy in Statistical Databases – PSD 2020, September 23–25, 2020, Tarragona, Catalonia. Springer: 353–368. DOI: https://doi.org/10.1007/978-3-030-57521-2 https://link.springer.com/chapter/10.1007/978-3-030-57521-2_25.
- Y. Wang, Y., X. Wu, and D. Hu. 2016. “Usings randomized response for differential privacy preserving data collection.” In Proceedings of the EDBT/ICDT 2016 Joint Conference, March 15–18, Bordeaux, France. DOI: https://doi.org/10.5441/002/edbt.2016.01; https://ceur-ws.org/Vol-1558/article35.Pdf.
- Warner, S.L. 1965. “Randomized response: a survey technique for eliminating evasive answer bias.” Journal of the American Statistical Association, 60(309): 63–69. DOI: https://doi.org/10.1080/01621459.1965.10480775.
- Wines, M. 2022. “The 2020 Census suggests that people live underwater.” There’s a reason. The New York Times, April 21. https://www.nytimes.com/2022/04/21/us/census-data-privacy-concerns.html (accessed February 2023).
- Winkler, W. 1999. The state of record linkage and current research problems. Technical report, Statistical Research Division, U.S. Census Bureau. Available at: https://courses.cs.Washington.edu/courses/cse590q/04au/articles/Winkler99.pdf (accessed February 27, 2023).