Project Grant 2523406
- 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's (NSF) Computer and Information Science and Engineering (CISE) program will provide $237,951 to the University of Washington (UW) to develop methods for making sensitive biomedical data more findable, accessible, interoperable, and reusable (FAIR) through the creation of open but privacy-preserving synthetic data sets. The goal is to address the "dark data" problem that hinders the development of beneficial AI...
- This Project Grant award from the National Science Foundation's Division of Computer and Network Systems under the Computer and Information Science and Engineering program (CFDA 47.070) provides $250,000 in funding to Rutgers, The State University of New Jersey to develop the fundamental principles for the systematic study of synthetic data. The research aims to establish properties of synthetic data, formal definitions of "synthetic", and a comprehensive evaluation framework including...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $279,959 to Carnegie Mellon University to advance the frontiers of differential privacy algorithms for private learning and synthetic data generation. The 5-year research project aims to develop a theoretical framework to better capture practical privacy scenarios, design practical privacy-preserving algorithms, and create auditing...
- This National Science Foundation project grant of $400,000 awarded on August 15, 2022 through July 31, 2025 under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) will fund research at Duke University to advance statistical and computational methods for releasing high-quality synthetic data as public use files. The research aims to develop novel techniques to improve disclosure risk assessment, quality verification for data analysts, and population generalizability when...
- This federal Project Grant award for $600,000.00 was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The award will fund research at the University of Washington (UW) to develop new privacy-preserving authentication techniques that limit the collection of personally identifiable information, a major challenge in digital applications. The project aims to advance the theory of blind signatures and anonymous...
- This National Science Foundation (NSF) Project Grant award for $239,966 under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program is focused on theoretical research to address challenges in high-dimensional probability and its applications in data science. The key objectives are to develop a rigorous mathematical framework for understanding the authenticity and privacy of synthetic data, as well as broaden the reach of random matrix theory in data science through new...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $100,000 to Northeastern University provides funding for a 3-year collaborative research project titled "SATC: CORE: SMALL: Empathy-Based Privacy Education and Design Through Synthetic Persona Data Generation." The project aims to develop new frameworks and tools to enhance privacy education and design through the use of AI-generated synthetic...
- This $209,368 federal Project Grant award from the National Science Foundation's Integrative Activities program (CFDA 47.083) aims to develop trusted, low-overhead tools that enable computation directly on encrypted data. The goal is to accelerate the creation of new capabilities that allow confidential data to be shared with untrusted parties who can extract insights without accessing the unencrypted data. This would increase public trust in modern AI tools and enable data-powered, socially...
- 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 Project Grant award, provided by the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop techniques for generating privacy-preserving synthetic data. The $274,847 award, spanning from October 1, 2025 to September 30, 2027, is a collaborative research effort between the University of Washington Tacoma and the University of Central Florida. The project seeks to advance the state-of-the-art in secure multi-party computation and fully homomorphic encryption protocols to train synthetic data generators while keeping the original data encrypted. This will enable organizations to share useful synthetic data without revealing the underlying sensitive information, especially in domains like healthcare where real data access is restricted due to privacy concerns. The project will also create valuable research opportunities for students, strengthening the future AI and cybersecurity workforce.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $274.8k | 8/19/25 |