Project Grant 2347253

Award Date 10/1/23
Completion Date 11/30/24
Dollars Obligated $149K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Rochester, NY 14623, USA
Similar Awards
This $404,113 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports research by The Ohio State University to develop application-aware homomorphic encryption (HE) hardware accelerators for privacy-preserving cloud computing and machine learning. The project aims to significantly speed up HE implementations by incorporating algorithmic reformulations, new HE operator designs, and cross-layer optimization...
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 $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 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 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 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...
This $274,356 Project Grant was awarded on September 1, 2023 by the National Science Foundation (NSF) under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) federal grant program. The funding will support the development of a secure image recognition and machine learning solution using advanced cryptography by L-Infinity Labs, Inc., a for-profit organization. The goal is to adapt existing deep neural network models to use fully homomorphic encryption in order to perform image...
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 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 (NSF) awarded a $108,385 Computer and Information Science and Engineering (CFDA 47.070) Project Grant to New York University (NYU) to develop novel hardware and software techniques to substantially improve the computational efficiency of privacy-preserving computation. The goal is to "speedup privacy-preserving computation to a practical level, providing users unparalleled privacy guarantees while simultaneously providing access to online services now...

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:

  1. Hardware accelerators for homomorphic encryption-based noise cancellation and contrast enhancement of images, leveraging numerical approaches for division and min/max operations on encrypted data.
  2. A custom hardware accelerator for an image-thresholding technique to highlight regions of interest in encrypted images, using numerical implementations of comparison operations.

This work aims to significantly reduce the computational runtime overhead compared to software-only homomorphic encryption approaches, enabling more practical applications of this privacy-preserving technology in areas like financial analytics, medical data analysis, and machine learning. The award recipient is the Rochester Institute of Technology (RIT), a private university with extensive experience supporting federal government research, development, and education initiatives.

Generated 8/6/24, 6:31 AM