Public Abstract

DE-SC0024884: DOE partial support for Research Coordination Network (RCN) for Privacy-Preserving Data Sharing and Analytics

Award Status: Active

Institution: FPF Education and Innovation Fund, Washington, DC
UEI: QB24LJLXJXM3
DUNS:

Most Recent Award Date: 04/16/2024
Number of Support Periods: 1
PM: Lee, Steven

Current Budget Period: 07/01/2024 - 06/30/2027
Current Project Period: 07/01/2024 - 06/30/2027
PI: Verdi, John

Supplement Budget Period: N/A

Public Abstract

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) that allow for data analysis without sacrificing privacy. While PETs can mitigate risk, there are many factors holding back these technologies from 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.

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) that allow for data analysis without sacrificing privacy. While PETs can mitigate risk, there are many factors holding back these technologies from 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.