Project Grant 2311648
Award Date 10/1/23
Completion Date 9/30/27
Dollars Obligated $600K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Albany, CA 94710, USA
Similar Awards
- This $200,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at San Diego State University Foundation (dba San Diego State University Research Foundation) to improve the efficiency and accuracy of sketching and streaming algorithms using machine learning. The key objectives are to: 1) Enhance existing sketching and streaming algorithms through machine learning to improve space and...
- This $175,000 two-year Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of novel local differential privacy techniques to significantly improve the privacy-utility tradeoff in multi-attribute data analysis. The Rochester Institute of Technology will develop techniques exploiting correlation in multi-attribute data and correlated random...
- This $13,843 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will fund research to develop new computer systems that allow organizations to gain insights from large datasets while keeping individual information completely private. The key research objectives are to: 1) develop new protocols for privacy-preserving data collection that enable servers to compute aggregate statistics over client data without...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $600,000 in funding to the University of California, Berkeley (UC Berkeley) to conduct research on streaming algorithms and data sketching. The project aims to make progress on fundamental problems in streaming, such as heavy hitters, quantiles, moment estimation, and sampling from data streams. The work will involve developing...
- This three-year $599,999 Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), will support research into novel methods for computing aggregate statistics on streaming data in a privacy-preserving manner. Specifically, the University of California, Los Angeles will explore efficient algorithms to privately compute telemetry data from user devices sending...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $279,959 to Carnegie Mellon University to advance the frontiers of differential privacy algorithms for private learning and synthetic data generation. The 5-year research project aims to develop a theoretical framework to better capture practical privacy scenarios, design practical privacy-preserving algorithms, and create auditing...
- The National Science Foundation (NSF) awarded a $400,000 Project Grant under its Computer and Information Science and Engineering (CISE) program to Georgia Tech Research Corporation, doing business as the Office of Sponsored Programs. The grant supports a 4-year collaborative research project to develop innovative, privacy-preserving machine learning algorithms for analyzing graph-structured data. Key objectives include designing non-uniform privatization protocols to balance data utility and...
- This $174,995 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The grant, awarded to the San Diego State University Research Foundation, aims to develop methods for applying differential privacy techniques to provenance graphs, which track the origin, usage, and modifications of data. The project has two main thrusts: 1) Identifying privacy risks in current provenance-based machine learning...
- This $209,988 Project Grant award from the National Science Foundation (NSF) Integrative Activities (CFDA 47.083) program supports research at Clemson University to establish theoretical and algorithmic foundations for ensuring differential privacy in decentralized optimization algorithms without losing provable optimality. The key research thrusts include: Investigating the tradeoff between convergence speed and differential privacy in decentralized optimization, Exploring differential...
- The National Science Foundation (NSF) awarded a $538,133 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Rutgers, The State University (Newark Division) to conduct research on privacy-preserving data analysis algorithms for continuously generated online data. The key focus of this 3-year project (August 1, 2025 - July 31, 2028) is to develop novel algorithms that can perform statistical analysis on sensitive user data while guaranteeing...