June 23, 2021

National Science Foundation (NSF) Awards $78.2 Million (225+ New Projects) to Support Cybersecurity, Privacy Research Including Award for Center for Trustworthy Machine Learning (CTML)

From NSF:

2018-10-24_17-55-58The National Science Foundation (NSF) Secure and Trustworthy Cyberspace (SaTC) program announces new support for a diverse, $78.2 million portfolio of more than 225 new projects in 32 states spanning a broad range of research and education topics, including artificial intelligence, cryptography, network security, privacy, and usability.

The new portfolio is headlined by an award for the Center for Trustworthy Machine Learning (CTML), which will address grand challenges in cybersecurity science and engineering that have the potential for broad economic and societal impacts. CTML is a Frontier project, a large-scale, multi-institution effort with work that crosses disciplines.

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Recent advances in machine learning have vastly improved the capabilities of computational reasoning in various domains, exceeding human-level performance in many tasks.

Despite these advances, significant vulnerabilities remain. Image recognition systems can be easily deceived, malware detection models can be evaded, and models meant to catch problems can be left vulnerable if they are attacked and manipulated while they’re being “trained.” The new Frontier CTML will work to develop an arsenal of defensive techniques for building future systems in a safer, more secure manner.

“This Frontier project will develop an understanding of vulnerabilities in today’s machine learning approaches, along with methods for mitigating against these vulnerabilities to strengthen future machine learning-based technologies and solutions,” Kurose said.

The $10 million, five-year CTML award will allow the center to focus on three interconnected and parallel thrusts of machine learning:

  • Investigating methods to defend a trained model from adversarial inputs.
  • Exploring rigorously grounded measures of model and training data robustness.
  • Identifying ways adversaries may abuse generative machine learning models and developing countermeasures for defending against such attacks.

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In addition to Penn State University, the CTML collaborating institutions include Stanford University, University of Virginia, University of California-Berkeley, University of California-San Diego, and the University of Wisconsin-Madison.

Review List of 225 New Projects Receiving Funding

Read the Complete Release

About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

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