This three-year Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $299,619 to the University of Virginia to lay the foundations for differentially private Internet measurement. Specifically, the award supports three main research thrusts. The first will study existing privacy issues in collecting and sharing Internet measurement data and develop an...
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 Project Grant titled "COLLABORATIVE RESEARCH: IMR: MM-1B: PRIVACY-PRESERVING DATA SHARING FOR MOBILE INTERNET MEASUREMENT AND TRAFFIC ANALYTICS" under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Board of Regents of the University of Nebraska, doing business as the University of Nebraska, in the amount of $170,000. The project aims to develop new methods for augmenting mobile internet...
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 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...
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 $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 $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research into information-theoretic privacy and security for personalized distributed learning systems at the University of California, Los Angeles from March 2022 through February 2025. The grant aims to design personalized learning models that leverage large-scale collaborative data while maintaining individuals' privacy and requiring trust only in one's own...
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...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program provides $640,348 to Carnegie Mellon University from February 2021 through September 2023 to support research into rethinking access pattern privacy from theory to practice. The award will fund collaborative work between Carnegie Mellon University and Cornell University to explore alternative notions of privacy and their relationships to established concepts like differential...