The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The U.S. Census Bureau Adopts Differential Privacy
Open accessAbstract
The U.S. Census Bureau announced, via its Scientific Advisory Committee, that it would protect the publications of the 2018 End-to-End Census Test (E2E) using differential privacy. The E2E test is a dress rehearsal for the 2020 Census, the constitutionally mandated enumeration of the population used to reapportion the House of Representatives and redraw every legislative district in the country. Systems that perform successfully in the E2E test are then used in the production of the 2020 Census. Motivation: The Census Bureau conducted internal research that confirmed that the statistical disclosure limitation systems used for the 2000 and 2010 Censuses had serious vulnerabilities that were exposed by the Dinur and Nissim (2003) database reconstruction theorem. We designed a differentially private publication system that directly addressed these vulnerabilities while preserving the fitness for use of the core statistical products.
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Cites
Revealing information while preserving privacy
Dinur, I., & Nissim, K. (2003) · Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/773153.773173 · Cited by 980
Privacy: Theory meets Practice on the Map
Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., & Vilhuber, L. (2008) · 2008 IEEE 24th International Conference on Data Engineering · doi:10.1109/icde.2008.4497436 · Cited by 435
Self-citation
Cited by
Dong, J., Roth, A., & Su, W. J. (2019) · arXiv · doi:10.48550/arxiv.1905.02383 · Cited by 389
Distributed Differential Privacy via Shuffling
Cheu, A., Smith, A., Ullman, J., Zeber, D., & Zhilyaev, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-17653-2_13 · Cited by 268
Randomness Concerns when Deploying Differential Privacy
Garfinkel, S. L., & Leclerc, P. (2020) · Proceedings of the 19th Workshop on Privacy in the Electronic Society · doi:10.1145/3411497.3420211 · Cited by 29
Making Machine Learning Forget
Shintre, S., Roundy, K. A., & Dhaliwal, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-21752-5_6 · Cited by 13
Angelopoulos, A. N., Bates, S., Zrnic, T., & Jordan, M. I. (2022) · Harvard Data Science Review · doi:10.1162/99608f92.16c71dad · Cited by 8
Metadata
- Authors
- Abowd, J. M.
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 2867-2867
- OpenAlex
- W2884738118
- Cited by
- 292
- References
- 2
- FWCI
- 8.63
- Type
- conference paper
- Language
- en
- Open access
- green
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