Project Grant 2413232

Award Date 4/1/25
Completion Date 3/31/28
Dollars Obligated $600K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Alafaya, FL 32816, USA
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This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) totaling $404,113 will support research and development to significantly improve the speed and practicality of homomorphic encryption (HE) for cloud computing and machine learning applications. The project aims to expand the privacy-preserving capabilities of cloud computing by developing new HE algorithms, operators, and hardware architectures...
This $218,301 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop new compiler techniques that enable scale management for Fully Homomorphic Encryption (FHE) programs. The key objectives are to: Propose a new scale management solution to minimize FHE program latency without costly iterative exploration (Task 1). Introduce an automatic bootstrapping management technique to...
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...
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) project grant of $209,368 awarded to the University of Central Florida (UCF) aims to develop trusted, low-overhead tools that enable computation directly on encrypted data. The project will explore a novel paradigm for circuit obfuscation and establish the security and efficiency of a proposed scheme for Encrypted Operator Computing (EOC), leveraging expertise across physics, mathematics, and computer science. The...
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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $600,000 to Northeastern University to conduct research on developing "doubly efficient" cryptographic tools for private information retrieval and encrypted data processing. The key aims of the 3-year project (7/15/2024 - 6/30/2027) are to create practical, efficient solutions for privacy-preserving data access and computations,...
This $800,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop a performant, cryptography-based approach to secure computation. The project will accelerate cryptographic schemes for fully homomorphic encryption, verifiable computation, and private information retrieval using a single hardware accelerator. The goal is to enable important use cases like outsourced computation,...
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...
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...

The National Science Foundation (NSF) awarded a $599,725 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Central Florida (UCF) to develop a system for verifiable fully homomorphic encryption (FHE). The goal is to create a solution that ensures both privacy and integrity for encrypted data processing on digital platforms like cloud computing, enabling safer and broader applications in sensitive domains.

The key products and services to be delivered through this 3-year grant include: (1) designing near-zero-cost verifiable algorithms for linear operations in FHE-enabled encrypted computations; (2) integrating an integrity-only trusted execution environment (TEE) to support verification for both linear and non-linear FHE operations; and (3) redesigning hardware with a clean-slate TEE architecture to address side-channel vulnerabilities and minimize encryption overhead. These innovations aim to significantly enhance the scalability and trustworthiness of privacy-preserving technologies for real-world applications. No subawards are planned for this project.

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