气候变化领域集成服务门户(CCPortal): How differential privacy will affect our understanding of health disparities in the United States

CCPortal

DOI	10.1073/pnas.2003714117
	How differential privacy will affect our understanding of health disparities in the United States
	Santos-Lozada A.R.; Howard J.T.; Verdery A.M.
发表日期	2020
ISSN	0027-8424
起始页码	13405
结束页码	13412
卷号	117 期号:24
英文摘要	The application of a currently proposed differential privacy algorithm to the 2020 United States Census data and additional data products may affect the usefulness of these data, the accuracy of estimates and rates derived from them, and critical knowledge about social phenomena such as health disparities. We test the ramifications of applying differential privacy to released data by studying estimates of US mortality rates for the overall population and three major racial/ethnic groups. We ask how changes in the denominators of these vital rates due to the implementation of differential privacy can lead to biased estimates. We situate where these changes are most likely to matter by disaggregating biases by population size, degree of urbanization, and adjacency to a metropolitan area. Our results suggest that differential privacy will more strongly affect mortality rate estimates for non-Hispanic blacks and Hispanics than estimates for non-Hispanic whites. We also find significant changes in estimated mortality rates for less populous areas, with more pronounced changes when stratified by race/ethnicity. We find larger changes in estimated mortality rates for areas with lower levels of urbanization or adjacency to metropolitan areas, with these changes being greater for non-Hispanic blacks and Hispanics. These findings highlight the consequences of implementing differential privacy, as proposed, for research examining population composition, particularly mortality disparities across racial/ethnic groups and along the urban/rural continuum. Overall, they demonstrate the challenges in using the data products derived from the proposed disclosure avoidance methods, while highlighting critical instances where scientific understandings may be negatively impacted. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Census; Demography; Differential privacy; Disclosure avoidance; Mortality
语种	英语
scopus关键词	article; avoidance behavior; Black person; Caucasian; controlled study; demography; ethnic group; ethnicity; health disparity; human; mortality rate; population size; privacy; United States; urbanization; ethnology; interpersonal communication; legislation and jurisprudence; mortality; population research; privacy; rural population; United States; urban population; Censuses; Disclosure; Ethnic Groups; Health Status Disparities; Humans; Mortality; Privacy; Rural Population; United States; Urban Population
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160913
作者单位	Santos-Lozada, A.R., Department of Human Development and Family Studies, Pennsylvania State University, University Park, PA 16802, United States; Howard, J.T., Department of Public Health, University of Texas at San Antonio, San Antonio, TX 78249, United States; Verdery, A.M., Department of Sociology and Criminology, Pennsylvania State University, University Park, PA 16802, United States
推荐引用方式 GB/T 7714	Santos-Lozada A.R.,Howard J.T.,Verdery A.M.. How differential privacy will affect our understanding of health disparities in the United States[J],2020,117(24).
APA	Santos-Lozada A.R.,Howard J.T.,&Verdery A.M..(2020).How differential privacy will affect our understanding of health disparities in the United States.Proceedings of the National Academy of Sciences of the United States of America,117(24).
MLA	Santos-Lozada A.R.,et al."How differential privacy will affect our understanding of health disparities in the United States".Proceedings of the National Academy of Sciences of the United States of America 117.24(2020).