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

SaTC：核心：小：了解隐私保护分析对实地的影响

• 批准号: 2319919
• 负责人: Alessandro Acquisti
• 金额: $ 59.49万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319919&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Understanding; Impact

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.

该项目调查了开发和部署“隐私保护分析”工具的下游影响。隐私保护分析是数学和统计技术（如差分隐私和联邦学习），旨在保护隐私，同时允许分析数据。该项目的目标是对隐私保护分析的影响进行评估。该项目的新奇在于补充了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）与社会科学的理论基础研究。虽然这些工具的实际应用日益增多，但关于其开发和部署的下游影响的应用研究却很少。该项目通过调查隐私保护分析在各种用例中的影响来解决这一差距。该项目更广泛的意义和重要性涉及到围绕隐私保护和数据实用性的学术和政策辩论。该项目可向决策者提供对这些工具在实际应用中的性能的评估。从管理的角度来看，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热门课题，但这一领域的大部分研究都集中在形式化方法和理论贡献上。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，为变革性研究做出贡献。该项目通过对其发展背后的组织和管理考虑及其对消费品、研究工作和政策制定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。这些研究将计算机科学和社会科学联系起来，并将不同的技术、组织和经济研究联系起来，以产生基于实践的证据。该奖项反映了NSF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。