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pro vyhledávání: '"Garfinkel, Simson"'
We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks: (1) developing general capabilities of the systems; (2) assuring the performance of AI systems and their training processes; (3) aligning system goals with human g
Externí odkaz:
http://arxiv.org/abs/2402.04464
Autor:
Abowd, John M., Adams, Tamara, Ashmead, Robert, Darais, David, Dey, Sourya, Garfinkel, Simson L., Goldschlag, Nathan, Kifer, Daniel, Leclerc, Philip, Lew, Ethan, Moore, Scott, Rodríguez, Rolando A., Tadros, Ramy N., Vilhuber, Lars
Using only 34 published tables, we reconstruct five variables (census block, sex, age, race, and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable tabulated at the census block level, at most 20.1% of reconstruc
Externí odkaz:
http://arxiv.org/abs/2312.11283
Autor:
Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Garfinkel, Simson, Heineck, Micah, Heiss, Christine, Johns, Robert, Kifer, Daniel, Leclerc, Philip, Machanavajjhala, Ashwin, Moran, Brett, Sexton, William, Spence, Matthew, Zhuravlev, Pavel
The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The a
Externí odkaz:
http://arxiv.org/abs/2204.08986
Autor:
Garfinkel, Simson, Stewart, Jonathan
Bulk_extractor is a high-performance digital forensics tool written in C++. Between 2018 and 2022 we updated the program from C++98 to C++17, performed a complete code refactoring, and adopted a unit test framework. The new version typically runs wit
Externí odkaz:
http://arxiv.org/abs/2208.01639
Autor:
GARFINKEL, SIMSON, STEWART, JON1
Publikováno v:
Communications of the ACM. Aug2023, Vol. 66 Issue 8, p44-52. 9p. 1 Color Photograph, 1 Chart.
Autor:
Abowd, John, Ashmead, Robert, Cumings-Menon, Ryan, Garfinkel, Simson, Kifer, Daniel, Leclerc, Philip, Sexton, William, Simpson, Ashley, Task, Christine, Zhuravlev, Pavel
Privacy-protected microdata are often the desired output of a differentially private algorithm since microdata is familiar and convenient for downstream users. However, there is a statistical price for this kind of convenience. We show that an uncert
Externí odkaz:
http://arxiv.org/abs/2110.13239
Autor:
Garfinkel, Simson L., Leclerc, Philip
Publikováno v:
19th Workshop on Privacy in the Electronic Society (WPES'20), November 9, 2020, Virtual Event, USA
The U.S. Census Bureau is using differential privacy (DP) to protect confidential respondent data collected for the 2020 Decennial Census of Population & Housing. The Census Bureau's DP system is implemented in the Disclosure Avoidance System (DAS) a
Externí odkaz:
http://arxiv.org/abs/2009.03777
Autor:
Garfinkel, Simson L., Love, J. Spencer
A file system standard for use with write-once media such as digital compact disks is proposed. The file system is designed to work with any operating system and a variety of physical media. Although the implementation is simple, it provides a a full
Externí odkaz:
http://arxiv.org/abs/2004.00402
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