Combinatorial Interaction Matching with Applications to Security and Data Analysis
This project will devise new combinatorial methods from discrete mathematics and apply the resulting novel analysis techniques in the domain of cybersecurity as a means to improve user privacy and in the domain of data analysis as a means to identify risk factors in medical and financial data. As a whole the project will improve important aspects of a digitalized modern society.
This project focuses on identification problems, formulated in terms of patterns on objects arising in discrete mathematics.
While CT is concerned with t-way interactions, we want to broaden the underlying structural building blocks available and move to the more general concept of patterns. Given a dataset and a specific pattern, a common problem is to find items in the dataset matching the pattern or vice-versa one can ask for patterns in data.
In the theoretical part of the project, we plan to advance the underlying combinatorial methods towards a more generic reasoning/identification framework in terms of pattern matching, while in the applied part we concentrate on pattern recognition and identification techniques for data sets arising in the medical or the financial domain.
Official Project Lead
MATRIS Research Group https://matris.sba-research.org/
The project is funded under the following financial assistance award 70NANB21H124 from U.S. Department of Commerce, National Institute of Standards and Technology.