Project Grant 2339686
Award Date
4/1/24
Completion Date
3/31/29
Dollars Obligated
$206K
Funding Federal Agency
Division of Computing and Communication Foundations
Awarding Federal Agency
Division of Computer and Network Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Chicago, IL 60616, USA
Similar Awards
This $524,981 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports Northeastern University's research on developing poisoning-resilient machine learning models for detecting cyber attacks. The key objectives are to create novel methods and software tools to investigate real-world poisoning attacks against supervised, semi-supervised, and unsupervised machine learning systems, and to design...
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...
This four-year $300,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop secure foundations for federated learning. Federated learning enables machine learning models to be collaboratively trained using data from many client devices without sharing private information. The researchers will investigate security vulnerabilities in federated learning's training phase, such as poisoning and backdoor attacks. They will...
The National Science Foundation (NSF) awarded a $400,000 Project Grant under its Computer and Information Science and Engineering (CISE) program to Georgia Tech Research Corporation, doing business as the Office of Sponsored Programs. The grant supports a 4-year collaborative research project to develop innovative, privacy-preserving machine learning algorithms for analyzing graph-structured data. Key objectives include designing non-uniform privatization protocols to balance data utility and...
This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to advance the security and privacy of on-device machine learning (ML) models used in Internet of Things (IoT)-driven scientific cyberinfrastructure.
The $600,000 award to Florida International University (FIU) has two primary goals: 1) developing a runtime detection and prevention mechanism to protect ML models from extraction attacks, and 2)...
This $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research into information-theoretic privacy and security for personalized distributed learning systems at the University of California, Los Angeles from March 2022 through February 2025. The grant aims to design personalized learning models that leverage large-scale collaborative data while maintaining individuals' privacy and requiring trust only in one's own...
This three-year, $300,000 project grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep...
The National Science Foundation awarded a $498,041 Project Grant to the Illinois Institute of Technology under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research on practical memory access pattern obfuscation approaches for current and emerging memory architectures from August 1, 2022 to July 31, 2025. The grant aims to develop integrated, cross-domain secure architecture designs and obfuscation schemes through algorithm, application,...
This $207,962 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) grant awarded to Tufts University on February 1, 2024 will support research to enhance the robustness of artificial intelligence (AI) systems against hardware-oriented vulnerabilities. The project has four main thrusts:
Designing algorithm-hardware collaborative backdoor attacks to exploit new adversarial vulnerabilities in deep neural networks.
Developing methodologies to...
This $300,000 National Science Foundation project grant supports research into robust machine learning under sparse adversarial attacks through 2025. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the University of California, Santa Barbara will develop theoretical frameworks and defense methods to make machine learning models resilient against perturbations affecting few data points. Specifically, the researchers aim to establish fundamental limits...