[
Abowd, J.R.A., R. Cumings-Menon, S. Garfinkel, M. Heineck, C. Heiss, R. Johns et al. 2022. “The 2020 Census Disclosure Avoidance System TopDown Algorithm.” Harvard Data Science Review 2. Available at: https://hdsr.mitpress.mit.edu/pub/7evz361i (accessed April 2023).
]Search in Google Scholar
[
Abowd, J.M., G.L. Benedetto, S.L. Garfinkel, S.A. Dahl, A.N. Dajani, M. Graham, M.B. Hawes et al. 2020. The modernization of statistical disclosure limitation at the U.S. Census Bureau, Available at: https://www.census.gov/library/working-papers/2020/adrm/CED-WP-2020-009.html (accessed April 2023).
]Search in Google Scholar
[
Bach, F. 2021. “Differential Privacy and Noisy Confidentiality Concepts for European Population Statistics.” Journal of Survey Statistics and Methodology 10(3): 642–687. DOI: https://doi.org/10.1093/jssam/smab044.eprint: https://academic.oup.com/jssam/article-pdf/10/3/642/44275540/smab044.pdf.
]Search in Google Scholar
[
Berners-Lee, T. 2015. Web Security––TLS Everywhere, not https: URIs Available at: https://www.w3.org/DesignIssues/Security-NotTheS.html (accessed April 2023).
]Search in Google Scholar
[
Chappell, B. 2020. Texas Supreme Court OKs state child abuse inquiries into the families of trans kids. Available at: https://www.npr.org/2022/05/13/1098779201/texas-supremecourt-transgender-gender-affirming-child-abuse.
]Search in Google Scholar
[
Daily, D. 2022. “Disclosure Avoidance Protections for the American Community Survey.” Random Samplings. Available at: https://www.census.gov/newsroom/blogs/-random-samplings/2022/12/disclosure-avoidance-protections-acs.html (accessed April 2023).
]Search in Google Scholar
[
Denning, D.E., and J. Schlöorer. 1980. “A Fast Procedure for Finding a Tracker in a Statistical Database.” ACM Trans. Database Systems 5(1): 88–102. DOI: https://doi.org/10.1145/320128.320138.
]Search in Google Scholar
[
Dick, T., C. Dwork, M. Kearns, T. Liu, A. Roth, G. Vietri, and Z.S. Wu. 2023. “Confidence-ranked reconstruction of census microdata from published statistics.” Proceedings of the National Academy of Sciences 120(8): e2218605120. DOI: https://doi.org/10.1073/pnas.2218605120.
]Search in Google Scholar
[
Diffie, W., and M. Hellman. 1976. “New directions in cryptography.” IEEE Transactions on Information Theory 22(6): 644–654. DOI: https://doi.org/10.1109/TIT.1976.1055638.
]Search in Google Scholar
[
Dinur, I., and K. Nissim. 2003. “Revealing Information While Preserving Privacy.” In Proceedings of the Twenty-second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems: 202–210. PODS ’03. June, San Diego, California: ACM. DOI: https://doi.org/10.1145/773153.773173.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Dwork, C., F. McSherry, K. Nissim, and A. Smith. 2006. “Calibrating Noise to Sensitivity in Private Data Analysis.” In Proceedings of the Third Conference on Theory of Cryptography: 265–284. TCC’06. New York, NY: Springer-Verlag. DOI: https://doi.org/10.1007/11681878_14.
]Search in Google Scholar
[
Dwork, C., and A. Roth. 2014. “The Algorithmic Foundations of Differential Privacy.” In Foundations and Trends in Theoretical Computer Science, 9: 211–407. 3–4. NOW. DOI: https://doi.org/10.1561/0400000042.
]Search in Google Scholar
[
Erlingsson, U., V. Pihur, and A. Korolova. 2014. “RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response.” In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security: 1054–1067. CCS ’14. Scottsdale, Arizona, USA: ACM. DOI: https://doi.org/10.1145/2660267.2660348.
]Search in Google Scholar
[
Fienberg, S.E., and J. McIntyre. 2005. “Data Swapping: Variations on a Theme by Dalenius and Reiss.” Journal of Official Statistics 21 (2): 309–323. Available at; https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/data-swapping-variations-on-a-theme-by-dalenius-and-reiss.pdf.
]Search in Google Scholar
[
Hansen, M. 2018. “To Reduce Privacy Risks, the Census Plans to Report Less Accurate Data.” The New York Times Available at: https://www.nytimes.com/2018/12/05/upshot/to-reduce-privacy-risks-the-census-plans-to-report-less-accurate-data.html (accessed April 2023).
]Search in Google Scholar
[
Hawes, M. 2021a. The Census Bureau’s Simulated Reconstruction-Abetted Reidentification Attack on the 2010 Census. Available: https://www.census.gov/data/academy/webinars/2021/disclosure-avoidance-series/simulated-reconstruction-abetted-re-identification-attackon-the-2010-census.html (accessed April 2023).
]Search in Google Scholar
[
Hawes, M. 2021b. The Census Bureau’s Simulated Reconstruction-Abetted Reidentification Attack on the 2010 Census. Available at: https://www.census.gov/data/academy/webinars/2021/disclosure-avoidance-series/simulated-reconstruction-abetted-re-identification-attackon-the-2010-census.html (accessed April 2023).
]Search in Google Scholar
[
Jarmin, R. 2019. Census Bureau Adopts Cutting Edge Privacy Protections for 2020 Census. Available at: https://www.census.gov/newsroom/blogs/random-samplings/2019/02/censusbureauadopts.html (accessed April 2023).
]Search in Google Scholar
[
Kenthapadi, K., N. Mishra, and K. Nissim. 2013. “Denials leak information: Simulatable auditing.” Journal of Computer and System Sciences 79 (8): 1322–1340. DOI: https://doi.org/10.1016/j.jcss.2013.06.004.
]Search in Google Scholar
[
Keyes, O., and A.D. Flaxman. 2022. “How Census Data Put Trans Children at Risk.” Scientific American Avialable at: https://www.scientificamerican.com/article/how-census-data-put-trans-children-at-risk/ (accessed April 2023).
]Search in Google Scholar
[
Kifer, D., J.M. Abowd, R. Ashmead, R. Cumings-Menon, P. Leclerc, A. Machanavajjhala, W. Sexton, and P. Zhuravlev. 2022. Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census. Technical report, CED Working Paper CED-WP-2022-004. Suitland, MD: Center for Enterprise Dissemination, US Census Burea. Available at: https://www.census.gov/library/working-papers/2022/adrm/CED-WP-2022-004.html (accessed April 2023).
]Search in Google Scholar
[
Kim, N. 2015. “The Effect of Data Swapping on Analyses of American Community Survey Data.” Journal of Privacy and Confidentiality 7(1). DOI: https://doi.org/10.29012/jpc.v7i1.644.
]Search in Google Scholar
[
Kleinberg, J., C. Papadimitriou, and P. Raghavan. 2000. “Auditing Boolean Attributes.” In Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems: 86–91. PODS ’00. Dallas, Texas, USA: ACM. DOI: https://doi.org/10.1145/335168.335210.
]Search in Google Scholar
[
Kuhn, T.S. 1962. The Structure of Scientific Revolutions. Chicago, IL: University of Chicago Press.
]Search in Google Scholar
[
Leclerc, P. 2019. “Results from a Consolidated Database Reconstruction and Intruder Re-Identification Attack on the 2010 Decennial Census.” In Challenges and New Approaches for Protecting Privacy in Federal Statistical Programs: A Workshop. June. Available at: https://sites.nationalacademies.org/cs/groups/dbassesite/documents/web-page/dbasse193509.pdf (accessed April 2023).
]Search in Google Scholar
[
McKenna, L. 2018. Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing. Technical report CDAR2018-01. U.S. Census Bureau. Available at: https://www.census.gov/library/working-papers/2018/adrm/ces-wp-18-47.html (accessed April 2023).
]Search in Google Scholar
[
McKenna, L. 2019. Disclosure avoidance techniques used for the 1960 through 2010 Decennial Censuses of Population and Housing Public Use Microdata Samples. Technical report. U.S. Census Bureau. Available at: https://www2.census.gov/adrm/CED/Papers/CY19/2019-04-McKenna-Six%20Decennial%20Censuses.pdf (accessed April 2023).
]Search in Google Scholar
[
Mervis, J. 2018. “Trump Officials Claim They Can Avoid 2020 Census Problems Caused by Controversial Citizenship Question. Experts Are Very Skeptical.” Science (4). DOI: https://doi.org/10.1126/science.aat8801.
]Search in Google Scholar
[
Mervis, J. 2019. “Can a Set of Equations Keep US Census Data Private?” Science (10). DOI: https://doi/10.1126/science.aaw5470.
]Search in Google Scholar
[
Muralidhar, K., and J. Domingo-Ferrer. 2023. See article in this issue.
]Search in Google Scholar
[
Ruggles, S., C. Fitch, D. Magnuson, and J. Schroeder. 2019. “Differential Privacy and Census Data: Implications for Social and Economic Research,” AEA Papers and Proceedings 109: 403–408. DOI: https://doi.org/10.1257/pandp.20191107.
]Search in Google Scholar
[
Shlomo, N. 2018. “Statistical Disclosure Limitation: New Directions and Challenges.” Journal of Privacy and Confidentiality 8(1). DOI: https://doi.org/10.29012/jpc.684. https://journalprivacyconfidentiality.org/index.php/jpc/article/view/684.
]Search in Google Scholar
[
U.S. Census Bureau. 2021. Disclosure Avoidance for the 2020 Census: An Introduction. Available at: https://www.census.gov/library/publications/2021/decennial/2020-census-disclosureavoidance-handbook.html.
]Search in Google Scholar
[
Wines, M. 2022. “The 2020 Census Suggests That People Live Underwater. There’s a Reason.” The New York Times Available at: https://www.nytimes.com/2022/04/21/us/-census-data-privacy-concerns.html (accessed April 2023).
]Search in Google Scholar
