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 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...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $191,421 to the University of California, San Diego (UCSD) to conduct research on developing new algorithms and analytical tools that enhance the utility and privacy accounting of differential privacy (DP) techniques. The key objectives of this two-year project are to: 1) unify recent breakthroughs in DP, such as Renyi DP and the Moments...
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...
The National Science Foundation (NSF) awarded a $179,055 Project Grant to the University of Virginia under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports collaborative research to develop privacy-preserving algorithms for fundamental problems in graph mining and network science. The project aims to create scalable, accurate graph differential privacy algorithms for applications like healthcare, social networks, finance, and computational...
This Project Grant award from the National Science Foundation (NSF) Division of Computer and Network Systems provides $182,515 to the University of Arizona to conduct collaborative research on graph mining and network science with differential privacy. The project aims to develop private, scalable algorithms for fundamental graph mining and network science problems, such as subgraph detection, node ranking, community detection, and analyzing graph dynamical systems like epidemic spread. The...
This National Science Foundation Project Grant of $450,000 will develop simulation-based statistical tools to improve analysis of differentially private data through July 2025. Funded by the Social, Behavioral, and Economic Sciences program (CFDA 47.075), the award supports research to deliver more accurate statistical estimation and inference for data subject to privacy constraints. The grantee, Purdue University, will use computer simulation techniques to address a wide range of statistical...
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 Project Grant award, valued at $199,997.00, was granted by the National Science Foundation (NSF) under its Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award, titled "Advancing Privacy and Security in Complex Networks by Statistical Algorithms: Safeguarding, Monitoring, and Remediation," focuses on improving data privacy and security within complex networks through a comprehensive strategy. The key products and services to be delivered under this...
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...