Project Grant 2317192
- 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 $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 $538,133 Project Grant, awarded by the National Science Foundation's (NSF) Division of Computer and Network Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), funds research on privacy-preserving statistical analysis of continuously generated sensitive data. The project, which runs from August 1, 2025 through July 31, 2028 and is performed at Rutgers University's Newark campus, develops novel algorithms that enable robust differential privacy...
- 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 National Science Foundation project grant of $300,000 will fund foundational research on differentially private Internet measurement from October 2022 to September 2025. Under the Computer and Information Science and Engineering program, researchers at the University of California Irvine will conduct three thrusts of work to lay the groundwork for deploying differential privacy in processing Internet measurement data. Specifically, Thrust 1 will study existing practices for collecting and...
- 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...
- The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Illinois for a 4-year collaborative research project on privacy-preserving machine learning on graph-structured data. The project aims to develop innovative, efficient algorithms for training and updating large-scale graph neural network models while preserving the privacy of sensitive graph data across applications in areas...
- 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...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $250,000 to the New Jersey Institute of Technology (NJIT) for a 3-year collaborative research project focused on developing advanced community detection and graph clustering methods for analyzing large network datasets. The key objectives are to create highly efficient software implementations of new community detection algorithms that can...
- The National Science Foundation awarded a $1.2 million Project Grant to Stanford University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) for work on the Foundations of Decentralized Data Science from July 1, 2022 to June 30, 2025. The award will support development of schemes for performing common data science tasks like analytics and inference on distributed data located across networks without collecting all data in a single location. The...
COLLABORATIVE RESEARCH: SATC: CORE: MEDIUM: GRAPH MINING AND NETWORK SCIENCE WITH DIFFERENTIAL PRIVACY: EFFICIENT ALGORITHMS AND FUNDAMENTAL LIMITS -DATA PRIVACY IS A FUNDAMENTAL CHALLENGE ACROSS NUMEROUS APPLICATIONS THAT RELY ON GRAPHS AND NETWORK DATA, INCLUDING HEALTHCARE, SOCIAL NETWORKS, FINANCE, AND COMPUTATIONAL EPIDEMIOLOGY. ADOPTING PRIVACY-PRESERVING SOLUTIONS TO PRACTICE IN SUCH APPLICATIONS IS OFTEN HINDERED BY THE LOSS IN UTILITY AND LACK OF SCALABILITY TO LARGE-SCALE PROBLEMS WITH BILLIONS OF NODES/EDGES. THIS PROJECT AIMS TO DEVELOP PRIVATE ALGORITHMS FOR SEVERAL FUNDAMENTAL PROBLEMS IN GRAPH MINING AND NETWORK SCIENCE, THAT CAN SCALE TO NETWORKS OF THE SIZE THAT ARISE IN REAL-WORLD APPLICATIONS AND PROVIDE GOOD ACCURACY BOUNDS. THE PROJECT?S BROADER SIGNIFICANCE AND IMPORTANCE ARE THAT PRIVATE ALGORITHMS WILL BECOME AVAILABLE TO A NEW COMMUNITY OF RESEARCHERS FROM PUBLIC-HEALTH POLICY PLANNING, CYBERSECURITY AND SOCIAL NETWORK ANALYSIS. ADOPTING GRAPH DIFFERENTIAL PRIVACY (DP) AS THE NOTION OF PRIVACY, THIS PROJECT ACHIEVES THE ABOVE GOALS THROUGH FUNDAMENTAL CONTRIBUTIONS IN PRIVACY-PRESERVING ALGORITHM DESIGN FOR VARIOUS FUNDAMENTAL PROBLEMS IN GRAPH MINING AND NETWORK SCIENCE, SUCH AS SUBGRAPH DETECTION, NODE RANKING, COMMUNITY DETECTION, AND STUDYING PROPERTIES OF GRAPH DYNAMICAL SYSTEMS SUCH AS EPIDEMIC SPREAD ON NETWORKS. THE PROJECT LEVERAGES TOOLS FROM DISTRIBUTED COMPUTATION, SUCH AS SAMPLING AND SKETCHING, AND DEVELOPS INNOVATIVE TOOLS FOR GRAPH DP TO YIELD HIGHLY-SCALABLE PRIVATE GRAPH ALGORITHMS WITH RIGOROUS ACCURACY BOUNDS (BOTH IN THEORY AND PRACTICE). FINALLY, THE PROJECT WILL LEAD TO THE DEVELOPMENT OF A PRIVATE GRAPH PROCESSING SYSTEM, WHICH WILL BE INCORPORATED INTO A NETWORK SCIENCE CYBER-INFRASTRUCTURE. ACCORDINGLY, THE TOOLS OF GRAPH DP WILL BE MADE AVAILABLE TO THE BROADER COMMUNITY OF NETWORK SCIENCE AND COMPUTATIONAL EPIDEMIOLOGY. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $110.3k | 9/10/25 | ||
| Not listed | $107.2k | 7/24/25 | ||
| Not listed | $94.7k | 8/31/23 | ||
| Not listed | $87.8k | 5/30/23 |