Differential Privacy and Digital Displacement of Communities of Color in Census 2020 Data: What Geographers Need to Know
Topics: Quantitative Methods
, Urban and Regional Planning
, Ethnicity and Race
Keywords: Differential Privacy, Census, Population Geography
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 11:20 AM (Eastern Time (US & Canada)) - 2/28/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 23
Authors:
Jason Jurjevich, University of Arizona
Nicholas Chun, University of Arizona
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Abstract
The U.S. Census Bureau implemented differential privacy to safeguard individual confidentiality in 2020 Census data products. Differential privacy intentionally distorts census data by injecting “noise” into publicly available data, making it more difficult for outside parties to potentially identify individuals. Noise-induced error is most severe for small-area geographies (e.g., census blocks), as well as for certain populations, including communities of color, principally because more noise is required to achieve an adequate level of privacy protection. Researchers face a difficult task unpacking the consequences of the new disclosure avoidance system. Our particular concern surrounds the efficacy and ethics of using census block data, which are critical to research, policy, and planning, and have long been used to create meaningful demographic neighborhood profiles. In this paper, we assess the efficacy of using differentially-private census block data to quantify gentrification and displacement in Washington, D.C. and Denver, Colorado. Our analysis uses the Privacy-Protected 2010 Census Demonstration Data to compare results under the traditional data avoidance system to the results from the differentially-private algorithm. We compare multiple geographies to convey the range of uncertainty using differentially-private data for longitudinal research. Results suggest that planners, public officials, and community groups should more carefully consider the underlying census geography when examining neighborhood-level change.
Differential Privacy and Digital Displacement of Communities of Color in Census 2020 Data: What Geographers Need to Know
Category
Virtual Paper Abstract
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