Project Grant 2317194

The National Science Foundation awarded a $200,657 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Maryland, College Park (UMD). The grant supports collaborative research to develop private algorithms for fundamental problems in graph mining and network science, such as subgraph detection, node ranking, community detection, and studying properties of graph dynamical systems like epidemic spread. The research aims to create highly scalable, privacy-preserving graph algorithms with rigorous accuracy bounds that can be adopted by researchers in public health policy planning, cybersecurity, and social network analysis. The project leverages tools from distributed computation to yield these privacy-preserving graph algorithms and will lead to the development of a private graph processing system to be incorporated into a network science cyberinfrastructure. The broader significance is that these private graph algorithms will become available to a new community of researchers across various domains relying on graph and network data.