NSF and DOE establish a Research Coordination Network dedicated to enhancing privacy research
In response to the rapidly evolving landscape of data collection and analysis driven by advances in artificial intelligence, the U.S. National Science Foundation and the U.S. Department of Energy (DOE) have established a Research Coordination Network (RCN) dedicated to advancing privacy research and the development, deployment and scaling of privacy enhancing technologies (PETs). Fulfilling a mandate from the "Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence," the initiative advances the recommendations in the National Strategy to Advance Privacy-Preserving Data Sharing and Analytics to move towards a data ecosystem where the beneficial power of data can be unlocked while protecting privacy.
"This crucial investment represents our commitment to advancing the foundations of responsible AI and privacy-enhancing technologies," said Dilma DaSilva, acting assistant director for NSF's Computer and Information Science and Engineering Directorate. "This effort supports research and development that enables individuals and society to benefit equitably from the value derived from privacy preserving data sharing and analytics."
"Privacy-enhancing technologies are increasingly important in today’s data-driven landscape. They allow us to safeguard sensitive datasets and information needed to advance a broad research, development, and demonstration portfolio," said Asmeret Asefaw Berhe, director of DOE’s Office of Science. "This Research Coordination Network will help us move toward the shared goal of establishing new standards for data safety and security that will allow us to continue to develop the innovations and scientific discoveries we need to achieve our clean energy and industrial goals."
Led by the Future of Privacy Forum Education and Innovation Foundation, this RCN brings together experts from academia, industry and government to support the development, deployment and scaling of PETs. These crucial technologies enable data analysis while safeguarding individual privacy and addressing concerns that arise due to the increasing sophistication of data analysis techniques.
One of the primary goals of the RCN is to address the barriers to widespread adoption of PETs, including regulatory considerations. By convening multidisciplinary, cross-sector and international expert groups, the RCN aims to understand the risks of data sharing and analytics for marginalized and vulnerable groups. Central to the RCN's mission is the examination of various mechanisms for deploying PETs, including research, technological innovations, and regulatory measures, and standards and certifications. The team will prioritize use cases for PETs that support privacy-preserving machine learning and those essential for federal agencies to ensure the equitable use of AI.
With support from NSF and DOE, the RCN will drive meaningful progress in the development and deployment of PETs, laying the foundation for a more privacy-conscious approach to data sharing and analytics in an era defined by rapid technological advancement.