Project Grant 2452834
- This Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) to Virginia Polytechnic Institute & State University supports the development of methods to allow organizations to safely share machine learning models trained on sensitive patient data without compromising individual privacy. The $128,985 project, running from October 2025 to September 2028, aims to create new techniques for auditing models, certifying their privacy...
- 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...
- 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 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 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...
- This Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports research to develop novel approaches for enabling participatory privacy protections for AI training data. The $120,511 award to the Fred Hutchinson Cancer Center in Seattle, WA will engage a multi-disciplinary team to conduct ethnographic and computational research. The key objectives are to: (1) identify socio-technical decision points and...
- The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Illinois for a 4-year collaborative research project on privacy-preserving machine learning on graph-structured data. The project aims to develop innovative, efficient algorithms for training and updating large-scale graph neural network models while preserving the privacy of sensitive graph data across applications in areas...
- 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 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 technologies, support AI innovation, and enable secure collaboration. The project will span from October 1, 2025 to September 30, 2028 and be performed by The Washington University, a leading private research institution. By addressing the challenge of balancing model sharing and patient privacy, this award serves the national interest across multiple domains.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $129.0k | 8/25/25 |