Project Grant 2529883
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program project grant, awarded to Purdue University for $600,000 on December 1, 2023, aims to improve the security and efficiency of secure multi-party computation (MPC) technology. MPC enables computations on private data without revealing non-essential information, facilitating collaboration in social, scientific, commercial, and security domains while maintaining privacy. The project will build a...
- This Project Grant award of $164,999 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is for a project titled "CRII: SATC: TOWARDS COST-EFFICIENT PRIVATE COMPUTATION FOR CLOUD DATA SCIENCE". The objective is to develop techniques that reduce the cost of practical Secure Multiparty Computation (MPC) for data science in the cloud. This will enable more cost-effective collaborative data science on private datasets, leading 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 Project Grant award of $275,000.00 from the National Science Foundation's Computer and Information Science and Engineering (CISE) program aims to develop modular hardware accelerators that enable secure and efficient deployment of fine-tuned foundation models on consumer devices. The award, made on September 1, 2025, supports research by George Mason University to address challenges in local deployment of large, computationally-intensive foundation models, which power a wide range of AI...
- This NSF CISE (CFDA 47.070) $600,000 Project Grant awarded to The Pennsylvania State University, doing business as Penn State, will develop methods to reduce privacy risks while enabling users to benefit from high-quality AI-based services. The key goals are to create mathematically-guaranteed privacy-preserving encodings or embeddings that can be used as input for AI-based applications, such as artistic image generation, medical diagnosis, and assistive technologies for the visually impaired....
- 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 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $374,291 to Brown University to develop novel tools and techniques for secure multi-party computation (MPC), with a focus on expanding the use cases of private set intersection (PSI). The project aims to bridge the gap between standard PSI and more enriched PSI functionalities, enabling large-scale, privacy-preserving data analytics across...
- 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...
- 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 $599,059 National Science Foundation project grant supports the development of secure multi-party computation programming technology at Rensselaer Polytechnic Institute. The goal is to advance compiler frameworks and optimization techniques that allow non-cryptography experts to write efficient privacy-preserving algorithms for applications like machine learning and data analytics. Key deliverables include an intermediate representation for secure computation and novel intra- and...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $600,000 to develop a hybrid solution that integrates the strengths of multi-party computing (MPC) and trusted hardware to enable secure and efficient AI offloading. The project, titled "SHF: SMALL: Practical Privacy-Preserving AI Offloading with Hybridizing Multi-Party Computing and Strongly-Secure Hardware," aims to address the significant privacy risks posed by users transmitting sensitive data to AI service providers. The hybrid approach will combine MPC's strong cryptographic security with specialized hardware that accelerates performance, providing a secure and efficient AI experience for users. The project will address key technical challenges in co-designing the new hardware and surrounding MPC system. This award has a performance period from November 1, 2025, to October 31, 2028, and is being conducted by The Pennsylvania State University.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 7/11/25 |