Research Coordination Network (RCN) for Privacy Preserving Data Sharing and Analytics

用于隐私保护数据共享和分析的研究协调网络 (RCN)

• 批准号: 2413978
• 负责人: John Verdi
• 金额: $ 49.01万
• 依托单位: FPF Education and Innovation Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2413978&HistoricalAwards=false
• 关键词: Research; Coordination; Network; RCN; Privacy

The advance of information technology, including artificial intelligence, has made the collection, analysis, and use of data central to research and economic activity. However, when those data are about people and what they do, there are risks to privacy that can grow with the sophistication of the analysis. In response, the Future of Privacy Forum Education and Innovation Foundation (FPF) is convening a Research Coordination Network (RCN) for Privacy Preserving Data Sharing and Analytics. The RCN is bringing together experts from academia, industry, and government to support the development, deployment, and scaling of Privacy Enhancing Technologies (PETs)—tools that allow for data analysis without sacrificing privacy. While PETsthey can mitigate risk, there are many factors holding back these technologies’ widespread use. One of the biggest stumbling blocks to the broader adoption of PETs in research is the current lack of clarity regarding how regulators will interpret and enforce privacy rules when organizations use them. The RCN is working to resolve this issue by bringing stakeholders together to discuss the use of PETs and explore how rules and standards can promote their appropriate use. The project team is convening a multidisciplinary, cross-sector, and international expert group of scholars and practitioners who focus on PETs development and use to understand the risks of data sharing and analytics for marginalized and vulnerable groups, along with civil rights and civil liberties writ large. This work is in direct response to recommendations from the National Strategy to Advance Privacy Preserving Data Sharing and Analytics. Further, the team is convening a secondary sub-network of high-level regulators from around the world that will inform and respond to the primary network, addressing the legal frameworks relevant to PETs adoption. With input from both groups, the project team is developing and disseminating new guidance to accelerate progress toward a privacy-preserving data-sharing and analytics ecosystem that advances democratic values and will foster convergence, characterize and strive to narrow persistent differences, and illuminate options to support broad deployment of PETs. The team is examining multiple mechanisms for this deployment, including via new technology, law and regulation, and/or standards and certifications. The team is particularly focused on use cases for PETs that support privacy-preserving machine learning and PETs that U.S. federal agencies may need to support the equitable use of AI.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.

包括人工智能在内的信息技术的进步使数据的收集、分析和使用成为研究和经济活动的核心。然而，当这些数据与人及其行为有关时，隐私风险就会随之增加。作为回应，隐私论坛的未来教育和创新基金会 (FPF) 正在召集一个隐私保护数据共享和分析研究协调网络 (RCN)，RCN 汇集了来自学术界、行业和行业的专家。政府对支持隐私增强技术 (PET) 的开发、部署和扩展，这些工具允许在不牺牲隐私的情况下进行数据分析，虽然 PET 可以降低风险，但有许多因素阻碍了这些技术的广泛使用。在研究中更广泛采用 PET 的一个原因是，目前监管机构在组织使用隐私规则时如何解释和执行隐私规则尚不明确。RCN 正在努力通过将利益相关者聚集在一起讨论 PET 的使用并探索来解决这一问题。项目团队正在召集一个由学者和从业者组成的多学科、跨部门和国际专家组，他们专注于 PET 的开发和使用，以了解边缘化群体和弱势群体的数据共享和分析的风险。这项工作是对推进隐私保护数据共享和分析国家战略的建议的直接回应，该团队正在召集来自高层监管机构的二级子网络。世界各地将通知并响应主要网络，解决与 PET 采用相关的法律框架，根据两个小组的意见，项目团队正在制定和传播新的指南，以加快实现民主价值观的隐私保护数据共享和分析生态系统的进展。​​并将促进融合，描述并努力缩小持续存在的差异，并阐明支持 PET 广泛部署的选项​​该团队正在研究这种部署的多种机制，包括通过新技术、法律和法规和/或标准。该团队特别注重认证。支持隐私保护机器学习的 PET 用例，以及美国联邦机构支持公平使用人工智能可能需要的 PET。该奖项的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。