10 citations · 23 across the 10 of their papers we have counts for
10 papers
SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)
William Sexton, Skye Berghel, Bayard Carlson +10
This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detai…
SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)
Sam Haney, Skye Berghel, Bayard Carlson +11
This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) o…
PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)
William Sexton, Skye Berghel, Bayard Carlson +10
This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and Housing Characteristics File (S-D…
Slowly Scaling Per-Record Differential Privacy
Brian Finley, Anthony M Caruso, Justin C Doty +5
We develop formal privacy mechanisms for releasing statistics from data with many outlying values, such as income data. These mechanisms ensure that a per-record differential priva…
Privately Answering Queries on Skewed Data via Per Record Differential Privacy
Jeremy Seeman, William Sexton, David Pujol +1
We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large en…
Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
Skye Berghel, Philip Bohannon, Damien Desfontaines +10
In this short paper, we outline the design of Tumult Analytics, a Python framework for differential privacy used at institutions such as the U.S. Census Bureau, the Wikimedia Found…
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…
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…
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity
Sam Haney, William Sexton, Ashwin Machanavajjhala +2
This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (D…
Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods
John M. Abowd, Ian M. Schmutte, William Sexton +1
With vast databases at their disposal, private tech companies can compete with public statistical agencies to provide population statistics. However, private companies face differe…