Algorithms and Private Data Analysis

点击翻译按钮获取中文摘要

The focus of this project is applications of discrepancy theory to computer science, with private data analysis and the design of approximation algorithms as a central application areas. Discrepancy theory studies the extent to which discrete structures, which are representable on a computer, can approximate continuous quantities, which are more amenable to analysis. Discrepancy theory raises fascinating computational questions and has connections to central areas of computer science, such as combinatorial optimization, data structures, and to the field of private data analysis, which develops methods for extracting useful information from data while preserving the privacy of individuals.