13 citations · 35 across the 7 of their papers we have counts for
7 papers
An Approximate Monte Carlo Simulation Method for Estimating Uncertainty and Constructing Confidence Intervals for 2020 Census Statistics
Robert Ashmead, Michael B. Hawes, Mary Pritts +2
To protect the confidentiality of the 2020 Census, the U.S. Census Bureau adopted a statistical disclosure limitation framework based on the principles of differential privacy. A k…
A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census
John M. Abowd, Tamara Adams, Robert Ashmead +12
We show that individual, confidential microdata records from the 2010 U.S. Census of Population and Housing can be accurately reconstructed from the published tabular summaries. Ni…
Disclosure Avoidance for the 2020 Census Demographic and Housing Characteristics File
Ryan Cumings-Menon, Robert Ashmead, Daniel Kifer +4
In "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Abowd et al. (2022) describe the concepts and methods used by the Disclosure Avoidance System (DAS) to produce f…
An In-Depth Examination of Requirements for Disclosure Risk Assessment
Ron S. Jarmin, John M. Abowd, Robert Ashmead +11
The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to m…
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…