SaTC: CORE: Small: Understanding the Impact of Privacy-Preserving Analytics on the Ground

该项目调查了“隐私保护分析”工具开发和部署的下游影响。隐私保护分析是一种数学和统计技术（如差分隐私和联邦学习），旨在保护隐私，同时允许对数据进行分析。该项目的目标是对隐私保护分析的影响进行评估。该项目的新颖之处在于用社会科学的实证研究来补充这一领域日益增长的理论研究（由计算机科学家和统计学家提出）。虽然这些工具的实际应用正在增长，但对其开发和部署的下游影响的应用研究却很缺乏。该项目通过调查各种用例中隐私保护分析的影响来解决这一差距。该项目更广泛的意义和重要性与围绕隐私保护和数据效用的学术和政策辩论有关。该项目可以为政策制定者提供这些工具在实际应用程序中的性能评估。从管理角度来看，该项目可以提供经验教训和可推广的最佳实践，供组织在探索隐私保护分析时考虑。虽然隐私保护分析是当代研究的一个热门主题，但该领域的许多研究都集中在形式方法和理论贡献上。该项目通过弥合隐私保护分析的理论分析与实地实证调查之间的差距，为变革性研究做出了贡献。该项目通过对其发展背后的组织和管理考虑以及它们对消费者产品、研究工作和决策的影响进行实证研究来做到这一点。这些研究利用了多种方法，包括定性调查研究、在线实地实验和观察研究。这些研究将计算机和社会科学联系起来，并将不同的技术、组织和经济研究流联系起来，以产生基于实践的证据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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. 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.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.