  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 facto
standard of privacy protection for the 2020 Census release. Enforcing
differential privacy involves adding carefully calibrated random noise to
sensitive demographic information prior to its release. This change has the
potential to impact policy decisions like political redistricting and other
high-stakes practices, partly because tremendous federal funds and resources
are allocated according to datasets (like Census data) released by the US
government. One under-explored yet important application of such data is the
redrawing of school attendance boundaries to foster less demographically
segregated schools. In this study, we ask: how differential privacy might
impact diversity-promoting boundaries in terms of resulting levels of
segregation, student travel times, and school switching requirements?
Simulating alternative boundaries using differentially-private student counts
across 67 Georgia districts, we find that increasing data privacy requirements
decreases the extent to which alternative boundaries might reduce segregation
and foster more diverse and integrated schools, largely by reducing the number
of students who would switch schools under boundary changes. Impacts on travel
times are minimal. These findings point to a privacy-diversity tradeoff local
educational policymakers may face in forthcoming years, particularly as
computational methods are increasingly poised to facilitate attendance boundary
redrawings in the pursuit of less segregated schools.
