This Project Grant award of $599,725 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, awarded on April 1, 2025, supports the development of a system for verifiable fully homomorphic encryption (FHE). The project aims to enhance the scalability and trustworthiness of privacy-preserving technologies by: (1) designing near-zero-cost verifiable algorithms for linear operations in FHE-enabled encrypted computations; (2) integrating an...
This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop new compiler techniques for managing scale in fully homomorphic encryption (FHE) programs. The $218,301 award, effective October 1, 2024 through September 30, 2027, supports three research tasks: 1) a new scale management solution to minimize FHE program latency, 2) an automatic bootstrapping management technique to optimize...
This $396,988 Project Grant award from the National Science Foundation (NSF) Division of Computer and Network Systems, under the CFDA 47.070 Computer and Information Science and Engineering program, supports research at Boston University to architect GPU (graphics processing unit) systems optimized for practical homomorphic encryption-based cloud computing. The project will explore acceleration of three different fully homomorphic encryption (FHE) schemes - BGV, B/FV, and CKKS - on GPUs to...
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...
The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program awarded a $174,999 Project Grant to the Illinois Institute of Technology (IIT) for the development of a novel programming and compilation framework to enable the adoption of Fully Homomorphic Encryption (FHE) in general privacy-preserving applications. The two-year grant, effective from July 1, 2025 to June 30, 2027, aims to create a domain-specific language and optimized compiler infrastructure...
This National Science Foundation (NSF) Division of Computer and Network Systems Project Grant, with the Catalog of Federal Domestic Assistance (CFDA) number 47.070, will support Northeastern University's research on architecting graphics processing units (GPUs) to accelerate practical homomorphic encryption (FHE)-based computing in cloud environments. The $395,843 award, spanning August 1, 2023 to July 31, 2026, will explore the use of GPUs to accelerate FHE computing across three different...
This $174,810 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports research at Northern Arizona University (NAU) to optimize resource-intensive operations in existing fully homomorphic encryption schemes and integrate the optimized algorithm into federated learning frameworks. The project aims to enhance security in Internet of Things communications by enabling secure data analysis on encrypted data...
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 (CFDA 47.070 - Computer and Information Science and Engineering) provides $291,703 to the Regents of the University of Minnesota to accelerate the development of practical and scalable privacy-preserving machine learning (ML) solutions. The project aims to create efficient, crypto-friendly neural architectures and algorithm-hardware co-design methodologies to significantly speed up privacy-preserving ML on hardware platforms like...
This $209,368 federal Project Grant award from the National Science Foundation's Integrative Activities program (CFDA 47.083) aims to develop trusted, low-overhead tools that enable computation directly on encrypted data. The goal is to accelerate the creation of new capabilities that allow confidential data to be shared with untrusted parties who can extract insights without accessing the unencrypted data. This would increase public trust in modern AI tools and enable data-powered, socially...