Debate over new census privacy measures overlooks larger issues with data error in Title I funding

Controversy surrounds the U.S. Census Bureau's new measures to preserve privacy, but a new study examines how existing data error can pose an even larger problem for evidence-based policies. The cornerstone of the Census Bureau's updated privacy measures, differential privacy, requires injecting statistical uncertainty, or noise, when sharing sensitive data. Scholars, politicians, and activists have raised concerns about the effect of this noise on crucial uses of census data. Yet most analyses of trade-offs around differential privacy overlook deeper uncertainties …

census computer sciences data error funding privacy