Project Grant 2312275
- This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $404,113 to The Ohio State University to develop application-aware homomorphic encryption algorithms and hardware accelerators for enabling privacy-preserving cloud computing and machine learning. The 3-year project, starting on December 15, 2024, aims to dramatically improve the speed of homomorphic encryption implementations, which...
- 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 $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 $149,156 project grant, awarded on October 1, 2023 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, will fund the development of custom hardware accelerators for privacy-preserving image processing using homomorphic encryption. The key products to be delivered include: Hardware accelerators for homomorphic encryption-based noise cancellation and contrast enhancement of images, leveraging numerical approaches for division and min/max...
- This two-year National Science Foundation project grant of $137,683 aims to enhance the reliability and privacy of graphics processing unit (GPU)-based deep learning computing. Funded under the Computer and Information Science and Engineering program, the award supports research at West Virginia University to address vulnerabilities in GPU architecture and design lightweight protection schemes. Specifically, the university researchers will explore vulnerabilities impacting GPU-based deep...
- This $599,725 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a system for verifiable fully homomorphic encryption (FHE) at the University of Central Florida. The project aims to ensure both the integrity and verifiability of FHE computations, enabling safer and broader applications of this privacy-preserving technology in sensitive domains. The key research thrusts include designing...
- 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...
- The National Science Foundation (NSF) awarded a $299,995 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Massachusetts (UMass) to develop novel security approaches for protecting field-programmable gate arrays (FPGAs) used in cloud computing environments. The key objectives of this 3-year project are to introduce secure operating mechanisms and a security and queue management unit (SQMU) to enable controlled sharing of FPGAs...
- This $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at Northeastern University to improve the security of machine learning models in multi-tenant cloud field programmable gate array (FPGA) environments. The three-year award aims to advance understanding of vulnerabilities in cloud-FPGAs shared by multiple tenants, where a malicious actor could potentially manipulate another tenant's machine learning...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award to the University of Southern California (USC) provides $599,992 from Oct 1, 2023 to Sep 30, 2026 to develop a portable and configurable library to enable secure, resilient, and trustworthy cyberinfrastructure for end-to-end privacy-sensitive machine learning (ML) inference. The key products and services to be delivered under this award include: An L1 library of FPGA-accelerated...
COLLABORATIVE RESEARCH: CSR: MEDIUM: ARCHITECTING GPUS FOR PRACTICAL HOMOMORPHIC ENCRYPTION-BASED COMPUTING -CLOUD COMPUTING HAS BECOME A POPULAR PATH FOR EFFICIENTLY SHARING COMPUTE RESOURCES. HOWEVER, THE CLOUD CAN BE AN UNSAFE COMPUTING ENVIRONMENT. TO PREVENT DATA EXPOSURE, ONE CAN USE FULLY HOMOMORPHIC ENCRYPTION (FHE)-BASED COMPUTING IN THE CLOUD. FHE PROVIDES STRONG DATA PRIVACY GUARANTEES BECAUSE IT ENABLES OPERATIONS ON ENCRYPTED DATA. UNFORTUNATELY, PROCESSING ENCRYPTED DATA USING FHE TAKES MULTIPLE ORDERS OF MAGNITUDE LONGER THAN PROCESSING UNENCRYPTED DATA DUE TO ITS PROHIBITIVELY HIGH COMPUTE AND MEMORY REQUIREMENTS. THIS PROJECT WILL EXPLORE THE USE OF GRAPHICS PROCESSING UNITS (GPUS) TO ACCELERATE FHE-BASED COMPUTING. WE CONSIDER THREE DIFFERENT FHE SCHEMES: BRAKERSKI-GENTRY-VAIKUNTANATHAN (BGV), BRAKERSKI/FAN-VERCAUTEREN (B/FV), AND CHEON-KIM-KIM-SONG (CKKS), THUS SUPPORTING OPERATIONS ON BOTH INTEGERS AND FLOATING-POINT NUMBERS. THIS PROJECT WILL ADVANCE THE STATE-OF-THE-ART IN GPU COMPUTE AND MEMORY ARCHITECTURES TO ENABLE PRACTICAL FHE-BASED COMPUTING IN THE CLOUD. WE WILL ALSO DELIVER NEW FHE BENCHMARKS FOR GPUS AND SIMULATION TOOLS. THE OUTCOMES OF THE PROPOSED RESEARCH WILL HAVE A DIRECT IMPACT ON THE DESIGN OF NEXT-GENERATION PRIVACY-PRESERVING COMPUTING SYSTEMS. WE WILL WORK WITH A NETWORK OF COMPANIES TO EVALUATE OUR WORK IN A PRACTICAL SETTING AND DISSEMINATE IT. WE WILL ALSO OPEN-SOURCE SOFTWARE AND TOOLS RESULTING FROM OUR WORK TO BENEFIT THE BROADER RESEARCH COMMUNITY. WE WILL ACTIVELY PARTICIPATE IN THE BROADENING PARTICIPATION IN COMPUTING PLANS AT BOTH NORTHEASTERN UNIVERSITY AND BOSTON UNIVERSITY, WHILE DEVELOPING A NUMBER OF NEW PROGRAMS TO ENGAGE A DIVERSE AUDIENCE. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $204.2k | 8/14/25 | ||
| Not listed | $395.8k | 7/21/23 |