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.

翻译：在数据和人工智能时代隐私威胁日益严峻的背景下，美国人口普查局近期采用了差分隐私这一隐私保护的行业标准，用于2020年人口普查数据的发布。实施差分隐私需要在发布敏感人口统计数据之前，添加经过精确校准的随机噪声。这一变化可能影响诸如政治选区重划等政策决策及其他高风险实践，部分原因在于联邦资金和资源的庞大分配依赖于美国政府发布的（如人口普查数据等）数据集。此类数据一个尚未充分探索但至关重要的应用是重新划分学校招生边界，以促进人口统计学层面更少隔离的学校。本研究提出以下问题：差分隐私如何影响旨在促进多样性的边界方案，具体表现为隔离程度、学生通勤时间及转学需求的变化？通过使用佐治亚州67个学区的差分隐私化学生数量模拟替代性边界方案，我们发现：提高数据隐私要求会降低替代性边界方案减少隔离、促进学校多元化和融合的程度，这主要源于边界调整下转学学生数量的减少。对通勤时间的影响微乎其微。这些发现揭示了地方教育政策制定者未来可能面临的隐私-多样性权衡困境，尤其是在计算方法日益成熟以助益通过招生边界调整追求低隔离学校的背景下。