scholarly journals Releasing Earnings Distributions using Differential Privacy

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Andrew David Foote ◽  
Ashwin Machanavajjhala ◽  
Kevin McKinney
Keyword(s):  
Differential Privacy ◽  
Building Blocks ◽  
Census Bureau ◽  
Prior Work ◽  
Post Secondary ◽  
Earnings Distributions ◽  
The U.S

The U.S. Census Bureau recently released data on earnings percentiles of graduates from post-secondary institutions. This paper describes and evaluates the disclosure avoidance system developed for these statistics. We propose a differentially private algorithm for releasing these data based on standard differentially private building blocks, by constructing a histogram of earnings and the application of the Laplace mechanism to recover a differentially-private CDF of earnings. We demonstrate that our algorithm can release earnings distributions with low error, and our algorithm out-performs prior work based on the concept of smooth sensitivity from Nissim et al. (2007).

scholarly journals Differential Privacy in the 2020 Census Will Distort COVID-19 Rates

2021 ◽  
Vol 7 ◽  
pp. 237802312199401
Author(s):  
Mathew E. Hauer ◽  
Alexis R. Santos-Lozada
Keyword(s):  
Differential Privacy ◽  
The United States ◽  
Mortality Rates ◽  
Census Bureau ◽  
Mortality Data ◽  
County Level ◽  
Specific Mortality ◽  
Population Counts ◽  
Privacy Budget ◽  
The U.S

Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.

Disproportionately Overrepresented: Women in Local Elected Offices

2018 ◽  
Vol 50 (3) ◽  
pp. 165-176 ◽  
Author(s):  
Ethan M. Bernick ◽  
Brianne Heidbreder
Keyword(s):  
United States ◽  
Maximum Likelihood ◽  
Gender Norms ◽  
The United States ◽  
Census Bureau ◽  
National Association ◽  
County Level ◽  
Political Factors ◽  
Using Data ◽  
The U.S

This research examines the position of county clerk, where women are numerically disproportionately over-represented. Using data collected from the National Association of Counties and the U.S. Census Bureau, the models estimate the correlation between the county clerk’s sex and county-level demographic, social, and political factors with maximum likelihood logit estimates. This research suggests that while women are better represented in the office of county clerk across the United States, when compared to other elective offices, this representation may be because this office is not seen as attractive to men and its responsibilities fit within the construct of traditional gender norms.

scholarly journals Differentially Private Attributed Network Releasing Based on Early Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuye Wang ◽  
Jing Yang ◽  
Jianpei Zhan
Keyword(s):  
Topological Structure ◽  
Differential Privacy ◽  
Probability Model ◽  
Prior Work ◽  
Data Utility ◽  
High Data ◽  
Early Fusion ◽  
Attributed Network ◽  
Analysis Of Social Networks ◽  
Attributed Networks

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

scholarly journals Planetarium commute accessibility in the United States of America

2021 ◽  
Author(s):  
Alexander A. Kaurov ◽  
Vyacheslav Bazhenov ◽  
Mark SubbaRao
Keyword(s):  
United States ◽  
United States Of America ◽  
Science Communication ◽  
The United States ◽  
Census Bureau ◽  
Global Pandemic ◽  
Commute Time ◽  
Pandemic Response ◽  
Zip Code ◽  
The U.S

The COVID-19 global pandemic unprecedently disturbed the education system in the United States and lead to the closure of all planetariums that were providing immersive science communication. This situation motivates us to examine how accessible the planetarium facilities were before the pandemic. We investigate the most important socioeconomic and geographical factors that affect the planetarium accessibility using the U.S. Census Bureau data and the commute time to the nearest planetarium for each ZIP Code Tabulated Area. We show the magnitude of the effect of permanent closure of a fraction of planetariums. Our study can be informative for strategizing the pandemic response.

Government Employees’ Experience and Expectation of COVID-19 Hardships: The Moderating Role of Gender and Race in the United States

2021 ◽  
pp. 027507402110492
Author(s):  
JungHo Park ◽  
Yongjin Ahn
Keyword(s):  
Gender Gap ◽  
The United States ◽  
Census Bureau ◽  
Government Employees ◽  
Income Loss ◽  
Gender And Race ◽  
Nationally Representative ◽  
Moderating Role ◽  
The U.S

This article examines government employees’ experience and expectation of socioeconomic hardships during the COVID-19 pandemic—employment income loss, housing instability, and food insufficiency—by focusing on the role of gender and race. Employing the Household Pulse Survey, a nationally representative and near real-time pandemic data deployed by the U.S. Census Bureau, we find that government employees were less affected by the pandemic than non-government employees across socioeconomic hardships. However, female and racial minorities, when investigated within government employees, have a worse experience and expectation of pandemic hardships than men and non-Hispanic Whites. Our findings suggest a clear gender gap and racial disparities in the experience and expectation of pandemic hardships.

A Critical Application—The U.S. Census Bureau

2014 ◽  
pp. 35-43
Author(s):  
Vincent P. Barabba ◽  
Ian I. Mitroff
Keyword(s):  
Census Bureau ◽  
The U.S

The Emergence of Distance Learning in Higher Education

2000 ◽  
pp. 143-156 ◽  
Author(s):  
Caroline Howard ◽  
Richard Discenza
Keyword(s):  
Higher Education ◽  
Distance Education ◽  
Distance Learning ◽  
Secondary Schools ◽  
Department Of Education ◽  
Post Secondary ◽  
On Line ◽  
Education Statistics ◽  
The U.S

Although distance learning is not a new phenomenon, recently there has been a huge jump in the number of organizations offering on-line instruction. The National Center for Education Statistics released a two-year survey on distance programs for higher education on behalf of the U.S. Department of Education. The survey reported that one-third of U.S. post secondary schools offered distance education in 1995, and an additional 25% planned to offer courses within the next three years.

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