Project Grant FA23862514028

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to accelerate the development of practical, efficient, and scalable privacy-preserving machine learning (ML) services. The $189,898 award to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, will fund research to create new cryptographic operators, accuracy-preserving neural network architectures, and...

This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070) is focused on accelerating privacy-preserving machine learning as a service. The key objectives are to: (1) orchestrate information representation and model sparsity in the encryption domain to reduce memory and computation footprint; (2) overcome high overhead associated with multi-party computation (MPC)-based solutions through techniques like...

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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program project grant, awarded to Purdue University for $600,000 on December 1, 2023, aims to improve the security and efficiency of secure multi-party computation (MPC) technology. MPC enables computations on private data without revealing non-essential information, facilitating collaboration in social, scientific, commercial, and security domains while maintaining privacy. The project will build a...

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 $268,000 National Science Foundation project grant supports the development of a consolidated framework for computational privacy and machine learning at Michigan State University from October 2022 through September 2026. The framework aims to comprehensively consider optimal tradeoffs between privacy protections and critical machine learning properties like predictive utility, fairness, and distributed learning. It will provide tools to protect data privacy for real-world machine...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is providing $349,009 to Texas A&M University-Central Texas and Arizona State University to develop a database architecture that integrates privacy regulations and machine learning workflows. The key products and services to be delivered under this 3-year award, which runs from January 2025 to December 2027, include: Creating a database system...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant, awarded to North Carolina State University, is focused on enhancing privacy in federated learning, which is an AI approach enabling knowledge sharing without compromising data privacy. The $220,258 grant, awarded on October 1, 2024, aims to address vulnerabilities in federated learning schemes that may leak sensitive information through improper privacy...

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...

This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program provides $458,346 to the University of Maryland, College Park to establish a scalable hardware and software platform called "BRIDGE" that enables secure and collaborative multi-party machine learning. The project aims to integrate hardware and software innovations, as well as security and privacy mechanisms, to support various forms of distributed and...