Awards

In this dissertation, we examine how differentially private algorithms can be used to compute statistics while attending to context specific social and political commitments. This inquiry is informed by three studies, each of which examines this question in distinct ways. In our first study, we consider ways to configure differentially private algorithms based on the privacy and accuracy concerns of data subjects and other stakeholders, while simultaneously attending to the values of privacy, fairness, and strategyproofness. Our second study considers how differentially private algorithms can be configured to attend to the context of public health, while providing policymakers with mobility statistics to inform decisions on lockdown mandates and other non-pharmaceutical interventions. Our third study examines how the absence of adequate information privacy protections create vulnerabilities that implicate decisional privacy in reproductive healthcare. Informed by these vulnerabilities, we provide guidance on how to safeguard these decisional privacy interests using a variety of mechanisms, including differential privacy. Throughout these studies, we develop theory and tools to effectively leverage differential privacy in practice. This work serves as guide for using mathematical approaches to attend to context specific social and political commitments in areas where statistical knowledge is essential to advancing the public good