CAREER: Algorithms, Incentives, and Policy for Data Privacy

Privacy concerns are becoming a major obstacle to using data, and it is often unclear how current regulations should translate into technology. As differential privacy and other privacy tools are brought to bear in practice, new challenges arise in ensuring these applications maintain the privacy guarantees intended by theory. How, then, should organizations utilize advances in privacy technologies to make use of potentially sensitive data? This project bridges the gap between theory and practice in the formal study of privacy by addressing new technical challenges that arise when theoretical privacy technologies are implemented in real-world settings.

This project addresses three main technical questions: Firstly, how should a data curator optimally allocate a fixed differential privacy budget across multiple analysis tasks, to trade off the value of accurate analysis with the privacy budget? Secondly, how do people reason about and value their privacy in practice, and how do these valuations change based on context of the data or analysis task? Thirdly, how should markets for personal data be regulated to protect individual privacy and population-level fairness, while still enabling valuable data-driven decision making? Answering these questions requires an interdisciplinary approach that integrates tools from computer science for differentially private algorithm design, economics to understand incentives of organizations and individuals, and public policy for regulation of privacy technologies. To ensure the broad impact of this research, this project also includes a significant educational and outreach component, including curriculum development, mentorship of students, and workshop organization aimed at improving diversity in graduate education.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.