ANNOUNCEMENT

NOBEL PRIZE 2026

This year's laureates, their fields, and the discoveries that earned them the prize.Live nowSee the winners
Moonbase
← Back to Awards
NSFDirectorate for Computer and Information Science and Engineering

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

Active
Institution
FPF Education and Innovation Foundation, DC
Principal investigator
Jules Polonetsky
Funding
$490K
Duration
2024–2027
Open portfolio

Moonbase score

Still being scored

Abstract

The advance of information technology, including artificial intelligence, has made the collection, analysis, and use of data central to research and economic activity. 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 they 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. 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. 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 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.

Research team

  • Jules PolonetskyPI
  • John VerdiFormer PI

LOADING MOONBASE SCORE

Directorate for Computer and Information Science and EngineeringSaTC: Secure and Trustworthy CyberspaceInformation Technology Researcprivacyconveninganalyticsdata sharingprivacy-preservingrulesrcnpreserving

Questions for FPF Education and Innovation Foundation

Answered by the team behind this research.

Same topic, scored first.