This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $189,898 to the Regents of the University of Minnesota to accelerate the development of efficient, scalable, and privacy-preserving machine learning (ML) services. The project aims to advance trustworthy artificial intelligence by creating new ML-specific cryptographic operators, accuracy-preserving neural architectures, and algorithm-hardware...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $241,682 to the Regents of the University of Michigan to develop a consolidated privacy protection framework for machine learning systems. The 2-year project, running from October 2024 to November 2026, will comprehensively consider the trade-offs between computational privacy and critical machine learning properties such as utility, fairness, and...
This $404,113 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to accelerate the development of privacy-preserving cloud computing and machine learning through application-aware homomorphic encryption (HE) techniques. The project, led by The Ohio State University, will pursue scalability improvements to HE by taking into account the specific computations involved in neural network applications...
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 $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at Northeastern University focused on developing doubly efficient Private Information Retrieval (DEPIR) and fully homomorphic encryption for random-access machines. The key goals of the project are to: 1) achieve practical efficiency for DEPIR and fully homomorphic encryption techniques, 2) explore alternative cryptographic...
This Project Grant award from the National Science Foundation's Division of Computing and Communication Foundations, under CFDA Program 47.070 "Computer and Information Science and Engineering", provides $225,441 to Harvard University to research fundamental limits of privacy-enhancing technologies. The goal is to develop new methods that optimize privacy-preserving techniques while minimizing distortion and bias, in order to enable more accurate, fair, and privacy-protected machine...
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...
The National Science Foundation awarded a $174,855 Project Grant to Michigan Technological University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant supports development of techniques to enable secure machine learning queries over encrypted databases in cloud computing. Specifically, the university will develop an index-aided approach employing encryption of individual data items and generation of secure index items to simultaneously achieve strong...
This $240,062 project grant, awarded by the National Science Foundation (NSF) through its Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), aims to develop efficient and scalable hardware architectures for privacy-preserving neural network inference based on ciphertext-ciphertext fully homomorphic encryption (FHE). The research will focus on designing optimized hardware building blocks, such as polynomial multipliers, and a reconfigurable FHE architecture that...
This Project Grant award, with a total funding amount of $374,291, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The award is focused on expanding the applications of secure multi-party computation (MPC), with a specific emphasis on enhancing private set intersection (PSI) techniques and applying them to a broader range of data analytics challenges. The key products and services to be...