ArticleProceedings of the National Academy of Sciences of the United States of America2020
How differential privacy will affect our understanding of health disparities in the United States.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Reproducibility of social science research using aggregate statistics with noise infused for differential privacy.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Expanding frameworks: Integrating vulnerability, exposure, and critical infrastructure to assess pluvial flood risks in New York City.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The shortcomings of synthetic census microdata.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evaluating bias and noise induced by the U.S. Census Bureau's privacy protection methods.Science advances · 2024Article
- A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Equitable differential privacy.Frontiers in big data · 2024Article
- When Privacy Protection Goes Wrong: How and Why the 2020 Census Confidentiality Program Failed.The journal of economic perspectives : a journal of the American Economic Association · 2024Article
- "It's None of Their Damn Business": Privacy and Disclosure Control in the U.S. Census, 1790-2020.Population and development review · 2023Article
- Impacts of census differential privacy for small-area disease mapping to monitor health inequities.Science advances · 2023Article
- Distribution-Invariant Differential Privacy.Journal of econometrics · 2023Article
- An Overview of Malaria Transmission Mechanisms, Control, and Modeling.Medical sciences (Basel, Switzerland) · 2022Review
- The effect of differential privacy on Medicaid participation among racial and ethnic minority groups.Health services research · 2022Article
- Priorities for successful use of artificial intelligence by public health organizations: a literature review.BMC public health · 2022Review
- Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward.JMIR medical informatics · 2022Article
- Balancing data privacy and usability in the federal statistical system.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- The 2020 US Census Differential Privacy Method Introduces Disproportionate Discrepancies for Rural and Non-White Populations.Population research and policy review · 2022Article
- Geospatial Perspectives on the Intersection of Chronic Disease and COVID-19.Preventing chronic disease · 2022Article
- The Role of Chance in the Census Bureau Database Reconstruction Experiment.Population research and policy review · 2022Article
- Reconstruction of age distributions from differentially private census data.Population research and policy review · 2022Article
- Data Flush.Harvard data science review · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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