Project Grant 2452833
Award Date 10/1/25
Completion Date 9/30/28
Dollars Obligated $129K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Blacksburg, VA, USA
Similar Awards
- This Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) provides $129,032.00 to develop methods for auditing and certifying the privacy guarantees of machine learning models trained on sensitive patient data. The research aims to create techniques that allow organizations to safely share these models without compromising individual privacy, in order to advance medical research and scientific discovery, enhance healthcare...
- This $150,000 Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) to Vanderbilt University Medical Center supports the development of methods to enable the safe sharing of machine learning models trained on sensitive healthcare data without compromising individual privacy. The key objectives are to evaluate the privacy properties of shared models, develop techniques for auditing and certifying their privacy guarantees, and provide...
- This National Science Foundation (NSF) Project Grant award under the Technology, Innovation, and Partnerships (CFDA 47.084) program provides $121,650 to Virginia Polytechnic Institute & State University (Virginia Tech) to develop the FLTEST testbed. The project aims to address challenges in verifying the privacy and robustness of federated learning systems, which enable AI model training across multiple data sources without directly sharing private data. The testbed will automate the...
- The National Science Foundation (NSF) awarded a $379,224 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Pennsylvania State University. The project, titled "CAREER: PRIVACY AUDITING FRAMEWORKS AND DEFENSES FOR MACHINE LEARNING MODELS TRAINED ON TABULAR DATA," aims to develop methods for assessing and mitigating privacy risks in machine learning (ML) models trained on sensitive tabular data, such as patient records or financial...
- This Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) provides $115,643.00 to develop FLTEST, an interdisciplinary testbed to automate privacy and robustness evaluations in federated learning systems. The project aims to address gaps in existing tools by developing automated test orchestration frameworks, implementing privacy attack simulation models, creating configuration vulnerability detection systems, and building...
- This $117,451 federal Project Grant awarded by the National Science Foundation (NSF) under the NSF Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports research to develop novel approaches that can help Public Interest Technology (PIT) organizations deploy data safeguards when building AI systems. The project engages a multi-disciplinary team to conduct ethnographic and computational research on using disclosure limitation techniques, including differential privacy, to...
- This $124,786 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop techniques to allow organizations to participate in the creation of privacy-preserving synthetic data without revealing their real data. The project, led by the University of Central Florida in partnership with the University of Washington Tacoma, will advance the state-of-the-art in secure multiparty computation and fully...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program, CFDA #47.070, provides $265,054 to Weill Medical College of Cornell University to develop a consolidated framework for computational privacy and machine learning from October 1, 2022 to September 30, 2026. The framework aims to comprehensively consider optimal tradeoffs between privacy protections and critical machine learning properties like predictive utility, fairness, and...
- This National Science Foundation (NSF) Project Grant award under the Technology, Innovation, and Partnerships (CFDA 47.084) program provides $1,297,636 to The Trustees of the University of Pennsylvania to develop the Trusted Integration Data Exchange System. This secure platform will enable government agencies, healthcare organizations, and research institutions to perform complex data analysis on jointly held sensitive datasets while maintaining compliance with data sharing policies and...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to develop a database architecture that integrates privacy regulations and compliance processes, enhances federated machine learning with decentralized data management functions, and automates privacy-model configuration in artificial intelligence workflows. The $249,998 award to Arizona State University will be used to address the...