NSF Award
2319919
The project investigates downstream implications of the development and deployment of tools for "privacy-preserving analytics." Privacy-preserving analytics are mathematical and statistical techniques (such as differential privacy and federated learning) designed to protect privacy while permitting analysis of data. The project's goal is to generate assessments of the impact of privacy-preserving analytics. The project’s novelty lies in complementing the growing body of theoretical research in this area (produced by computer scientists and statisticians) with social science empirically-grounded research. While practical applications of these tools are growing, there is a paucity of applied research on the downstream implications of their development and deployment. The project addresses that gap by investigating the impacts of privacy-preserving analytics across a variety of use cases. The project's broader significance and importance pertain to the scholarly and policy debate around privacy protection and data utility. The project can provide policy makers with an assessment of the performance of such tools in real-world applications. From a managerial perspective, the project can provide lessons learned and generalizable best practices for organizations to consider in their exploration of privacy-preserving analytics.<br/> <br/>While privacy-preserving analytics are a popular subject of contemporary research, much of the research in this area focuses on formal methods and theoretical contributions. The project contributes transformative research by bridging the gap between theoretical analyses of privacy-preserving analytics and their empirical investigation on the ground. The project does so by conducting empirical studies of the organizational and managerial considerations behind their development, and their impact on consumer products, research efforts, and policy-making. The studies leverage multiple methodologies, including qualitative survey research, online field experiments, and observational research. The studies connect computer and social sciences and tie different streams of technical, organizational, and economic research to produce evidence grounded in practice.<br/><br/>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.
