16 citations · 34 across the 4 of their papers we have counts for
4 papers
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Daniel Kifer, John M. Abowd, Robert Ashmead +5
The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, R…
Geographic Spines in the 2020 Census Disclosure Avoidance System
Ryan Cumings-Menon, John M. Abowd, Robert Ashmead +5
The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geo…
An Uncertainty Principle is a Price of Privacy-Preserving Microdata
John Abowd, Robert Ashmead, Ryan Cumings-Menon +7
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…
Randomness Concerns When Deploying Differential Privacy
Simson L. Garfinkel, Philip Leclerc
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…