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和经济活动中心的数据收集，分析和使用。但是，当这些数据与人及其所做的事情有关时，隐私的风险可能会随着分析的社会化而增长。作为回应，隐私论坛教育与创新基金会（FPF）的未来正在召集一个研究协调网络（RCN），以保存数据共享和分析。 RCN正在将来自学术界，行业和政府的专家汇集在一起​​，以支持隐私增强技术（PET）的开发，部署和扩展 - 允许无需牺牲隐私而进行数据分析的工具。尽管宠物可以减轻风险，但有许多因素阻止这些技术的宽度使用。在研究中，更广泛采用宠物的最大绊脚石之一是目前缺乏有关监管机构在组织使用时如何解释和执行隐私规则的清晰度。 RCN正在通过将利益相关者聚集在一起讨论宠物的使用并探讨规则和标准如何促进其适当的使用来解决这个问题。该项目团队正在召集一个由学者和从业人员组成的多学科，跨部门和国际专家小组，他们专注于宠物开发，并用于了解边缘化和脆弱群体的数据共享和分析的风险，以及公民权利和公民自由的书籍。这项工作直接回应了国家战略的建议，以提高保留数据共享和分析的隐私性。此外，该团队正在召集来自世界各地的高级监管机构的二级子网，该子网将告知和响应主要网络，以解决与宠物采用相关的法律框架。随着两个小组的投入，项目团队正在制定和传播新的指导，以加速进步，迈向隐私保护数据共享和分析生态系统，这些生态系统可以提高民主价值观，并将促进融合，表征和努力狭窄的持久差异，并照亮选项以支持宠物的广泛部署。该团队正在研究该部署的多种机制，包括通过新技术，法律和/或标准和认证。团队尤其专注于支持保护隐私的机器学习和美国联邦机构可能需要支持AI的宠物的宠物用例。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准通过评估来获得的支持。