Project Grant 2601526
- This federal Project Grant award for $538,133, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to design fast and efficient algorithms for analyzing sensitive data while preserving user privacy. The project, led by Rutgers, The State University, focuses on developing privacy-preserving algorithms for continual data analysis using the differential privacy framework. The goal is to make...
- The National Science Foundation (NSF) awarded a $581,966 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Pittsburgh. The grant, awarded on May 1, 2024, with a completion date of April 30, 2027, supports the development of a hardware-software co-design framework to address performance and memory space issues for privacy protection in cloud-based deep learning recommendation systems (DLRMs). Key project objectives include: 1)...
- This National Science Foundation (NSF) Project Grant award, funded under the Engineering program (CFDA 47.041), supports research to develop a novel framework for privacy-aware and fair data acquisition in multi-agent distributed systems. The $439,961 award to the University of California, Santa Barbara aims to create fair incentives for strategic agents to contribute an appropriate share of private data, enabling efficient and safe operation of critical distributed systems like autonomous...
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $199,993 to the University of North Dakota (UND) to develop a novel data synthesis framework that enables edge devices to generate privacy-preserving synthetic network traffic data. The framework aims to improve real-time performance and reliability of edge AI applications, such as anomaly detection and traffic classification, while addressing challenges like outdated data, privacy...
- This Project Grant award of $174,995.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program is focused on developing practical solutions for applying differential privacy techniques to provenance graphs. The project aims to 1) identify privacy risks in current provenance-based machine learning anomaly detectors, and 2) design a subgraph synthesis method to generate differentially private provenance graphs. Provenance tracking...
- 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 $500,000 Project Grant awarded by the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to Clemson University is for developing curriculum, hands-on labs, and a research platform to engage students in privacy compliance education. The project aims to cultivate a strong privacy-aware mindset among students, equipping them with the skills and knowledge needed to assess privacy compliance in user-centric apps across various platforms. Key deliverables...
- 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...
- This Project Grant award of $175,000.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program will support a research project titled "COMBINING GUARANTEED PRIVACY AND RESILIENCY IN DYNAMIC CYBER-PHYSICAL SYSTEMS." The project, conducted by The University of Tulsa, aims to develop a framework that combines guaranteed privacy protection with robust control strategies to enable secure and resilient operation of dynamic...
- The National Science Foundation awarded a $600,000 Project Grant to the Trustees of Boston University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year award will support research into developing new differentially private stochastic optimization algorithms for training neural networks while preserving individual privacy. Specifically, the grantee will investigate fundamental tradeoffs between privacy and performance in modern...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $250,000.00 to the University of Alabama to conduct collaborative research on realizing differential privacy mechanisms directly in embedded memories. The goal is to develop a privacy-by-design approach that can improve performance, power efficiency, and chip overhead across electronic devices, without relying on dedicated software. This innovative research aims to address critical privacy challenges in low-end devices and sensitive applications like surveillance and IoT. The project, running from January 1, 2026 to November 30, 2026, is expected to have a transformative impact on data privacy, cybersecurity, and the competitiveness of U.S. chip vendors.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $250.0k | 12/4/25 |