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In the face of increasingly severe privacy threats in the era of data and AI,the US Census Bureau has recently adopted differential privacy, the de factostandard of privacy protection for the 2020 Census release. Enforcingdifferential privacy involves adding carefully calibrated random noise tosensitive demographic information prior to its release. This change has thepotential to impact policy decisions like political redistricting and otherhigh-stakes practices, partly because tremendous federal funds and resourcesare allocated according to datasets (like Census data) released by the USgovernment. One under-explored yet important application of such data is theredrawing of school attendance boundaries to foster less demographicallysegregated schools. In this study, we ask: how differential privacy mightimpact diversity-promoting boundaries in terms of resulting levels ofsegregation, student travel times, and school switching requirements?Simulating alternative boundaries using differentially-private student countsacross 67 Georgia districts, we find that increasing data privacy requirementsdecreases the extent to which alternative boundaries might reduce segregationand foster more diverse and integrated schools, largely by reducing the numberof students who would switch schools under boundary changes. Impacts on traveltimes are minimal. These findings point to a privacy-diversity tradeoff localeducational policymakers may face in forthcoming years, particularly ascomputational methods are increasingly poised to facilitate attendance boundaryredrawings in the pursuit of less segregated schools.

Impacts of Differential Privacy on Fostering more Racially and Ethnically Diverse Elementary Schools