Project Grant 2452835
- 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 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 $350,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of advanced, privacy-preserving generative AI models for anonymizing biometric signals. The project aims to create modular and scalable anonymization methods suitable for both clinical and wearable device bio-signals, which can be customized for diverse demographics and health conditions. This will help preserve...
- This $193,885 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a privacy-preserving framework for sharing and learning from scarce medical data, specifically focused on videos of tracheostomy-dependent children. The 3-year project, running from October 1, 2025 to September 30, 2028, will establish a comprehensive suite of privacy mechanisms to enable the secure sharing of sensitive video...
- This $405,917 Project Grant was awarded on October 1, 2025 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The grant establishes a framework for securely sharing sensitive medical video data, such as footage of tracheostomy-dependent children, between healthcare providers in a privacy-preserving manner. The project brings together advanced technologies like face identification, video pixelation, and...
- 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...
- The National Science Foundation awarded a $900,000 Project Grant under the Integrative Activities program (CFDA 47.083) to the University of California, Los Angeles (UCLA) to create the first end-to-end provenance framework for medical artificial intelligence (AI) systems. The framework will enable tracing, auditing, and efficient removal of compromised or sensitive data from medical AI models, thereby improving patient privacy, reliability, and trustworthiness of these critical systems. The...
- This $173,754 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop an innovative privacy-preserving federated learning (FL) framework suitable for heterogeneous edge devices. The key objectives are to: 1) enable tailored device-specific models to mitigate biases and enhance performance across diverse computational capabilities and data distributions, 2) utilize differential privacy...
- 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 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 $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 tools to address any identified privacy issues. This work aims to address the challenge of balancing the need to share models for advancing medical research and scientific discovery with the imperative of protecting patient privacy. The project's outcomes are intended to advance medical research, enhance national health and prosperity through improved healthcare technologies, support American competitiveness in AI innovation, and enable secure collaboration while safeguarding personal privacy.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/25/25 |