This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $249,998 to Arizona State University (ASU) to develop a novel database architecture that integrates artificial intelligence/machine learning (AI/ML) capabilities, privacy regulations, and federated learning. The key products and services to be delivered under this 3-year project include: (1) a database system that coordinates data privacy...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant, awarded to North Carolina State University, is focused on enhancing privacy in federated learning, which is an AI approach enabling knowledge sharing without compromising data privacy. The $220,258 grant, awarded on October 1, 2024, aims to address vulnerabilities in federated learning schemes that may leak sensitive information through improper privacy...
The National Science Foundation (NSF) awarded a $189,898 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the Regents of the University of Minnesota to accelerate privacy-preserving machine learning (ML) as a service. The project aims to develop efficient, scalable, and encryption-conscious computing paradigms for practical privacy-preserving ML, including new ML-specific cryptographic operators, accuracy-preserving and crypto-friendly neural...
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...
The National Science Foundation (NSF) awarded a $387,044 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Arizona State University to improve the fundamental limits of privacy-enhancing technologies (PETs). The research aims to develop new PET methods that optimize the balance between preserving individual privacy and enabling comprehensive data analysis for societal benefit in domains such as healthcare, education, and resource allocation. Key...
This $299,886 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new big data algorithms that are robust to adversarial input. The award supports research to address emerging vulnerabilities in areas such as black-box streaming algorithms, white-box streaming algorithms, and adaptive data analysis with bounded space. This work will focus on improving the reliability, security, and trustworthiness of...
This $167,158 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a customizable, privacy-preserving database analytics system compatible with existing SQL databases. The key products to be delivered under this 4-year award include: Automated tools for analyzing a database schema and interactively developing a flexible privacy model to determine which data elements require differential...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a total funding of $174,770, supports the development of an adaptive, federated, continuous learning system that uses a novel federated, semi-supervised learning framework. This framework aims to retrain deep neural network models on distributed, unlabeled, heterogeneous data from edge devices, while leveraging explainable AI techniques to expedite local training. The...
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 $423,204 Project Grant under the Computer and Information Science and Engineering (CISE) program to The Pennsylvania State University (Penn State) for a 4-year collaborative research project titled "COLLABORATIVE RESEARCH: SATC: CORE: MEDIUM: DIFFERENTIALLY PRIVATE SQL WITH FLEXIBLE PRIVACY MODELING, MACHINE-CHECKED SYSTEM DESIGN, AND ACCURACY OPTIMIZATION." The goal is to develop an open-source, customizable system for preserving privacy...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) for $349,009 provides funding to Texas A&M University-Central Texas to develop a database architecture that integrates privacy regulations and machine learning (ML) workflows. The key products and services to be delivered under this project grant include:
Creating a database system that coordinates data privacy policies, AI/ML workflows, and regulatory compliance to ensure seamless alignment between data management and privacy regulations.
Building a federated data management framework to enhance incentive mechanisms, detect malicious gradients, and balance data distributions in federated learning, improving its accuracy and robustness.
Developing query and storage optimizers that automatically select appropriate privacy-preserving model architectures and storage schemes to meet user utility and privacy objectives in end-to-end AI/ML workflows.
The project aims to address shortcomings in current database architectures that prioritize speed and scalability over privacy, and to leverage the partnership between Texas A&M University-Central Texas and Arizona State University to train underrepresented students in the intersection of AI/ML, privacy, and database systems. The award period runs from January 2025 to December 2027.