Project Grant 2523439
- 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 federal Project Grant award of $599,040.00 was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to Kansas State University. The project, titled "COLLABORATIVE RESEARCH: SATC 2.0: RES: A PRIVACY-PRESERVING FRAMEWORK FOR SHARING AND LEARNING FROM SCARCE MEDICAL DATA", aims to establish a framework for securely sharing sensitive medical data, such as videos of tracheostomy-dependent children, between...
- 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 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's Integrative Activities (CFDA 47.083) program provides $611,959 to Meharry Medical College from September 1, 2024 to August 31, 2027. The project, titled "A Trustworthy and Privacy-Preserving Framework for Latency-Sensitive Video-Enabled Health Incident-Response Applications," aims to develop a novel AI-powered framework for trustworthy and privacy-preserving video analysis to enable rapid emergency response in 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 National Science Foundation (NSF) Project Grant award, under the CFDA 47.070 Computer and Information Science and Engineering program, provides $250,000 in funding to Arizona State University to develop intelligent anonymization methods for preserving the privacy of clients' bio-signals while retaining data utility for clinical purposes. The key products and services delivered through this 3-year award (10/1/2024 - 9/30/2027) include: 1) Designing reinforcement learning-guided generative...
- 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 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 $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 data between healthcare providers, while providing formal privacy guarantees that can be explained to caretakers. The research team at Louisiana State University Health Sciences Center New Orleans will leverage advanced technologies such as face identification, adversarial generative privacy mechanisms, and AI-driven text-to-video generation to create this unified system tailored for real-world application in medical settings and beyond.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $193.9k | 8/19/25 |