题目：Privacy and Utility of Geographic Data: Revealing, Evaluating, and Mitigating the Externalities of Geographic Privacy Protection
报告摘要：Geographic data is increasingly important in fields such as demography, public health, and transportation. However, the use of this data has raised privacy concerns, which have been intensified by recent technological advancements in location tracking and geospatial data analytics. Efforts to address these concerns include restricted access to data, anonymization and aggregation, and data perturbation. But these methods can limit the availability, transparency, and accuracy of geographic data, leading to externalities that affect data usage. This study proposes a methodological framework with three key components to address the externalities of geographic privacy protection: a synthetic data generator, measures to quantify privacy and utility, and spatial optimization models to find optimal solutions for producing and protecting useful geographic data. The proposed framework is validated using U.S. Census data and provides practical strategies for extending geographic privacy protection in real-world applications.