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

包括人工智能在内的信息技术的进步使得数据的收集、分析和使用成为研究和经济活动的核心。然而，当这些数据是关于人和他们所做的事情时，随着分析的复杂性，隐私风险可能会增加。 作为回应，隐私未来论坛教育和创新基金会（FPF）正在召集一个研究协调网络（RCN），用于隐私保护数据共享和分析。 RCN汇集了来自学术界，工业界和政府的专家，以支持隐私增强技术（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。 在研究中更广泛地采用PET的最大障碍之一是目前缺乏明确的监管机构将如何解释和执行隐私规则时，组织使用它们。RCN正在努力解决这个问题，将利益相关者聚集在一起讨论PETS的使用，并探索规则和标准如何促进其适当使用。该项目团队正在召集一个多学科，跨部门和国际专家组的学者和从业者谁专注于PETS的开发和使用，以了解数据共享和分析的边缘化和弱势群体的风险，沿着公民权利和公民自由的大。这项工作是对《推进隐私保护数据共享和分析国家战略》建议的直接回应。此外，该小组正在召集来自世界各地的高级监管机构组成的二级子网络，该网络将向主要网络提供信息并作出回应，解决与采用PET相关的法律的框架。在这两个小组的投入下，项目团队正在制定和传播新的指导方针，以加快隐私保护数据共享和分析生态系统的进展，该生态系统将促进民主价值观，并将促进融合，描述并努力缩小持续存在的差异，并阐明支持广泛部署PET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/或标准和认证。该团队特别关注支持隐私保护机器学习的PET用例，以及美国联邦机构可能需要支持公平使用人工智能的PET。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。

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.