Algorithms and Private Data Analysis

算法和私有数据分析

• 批准号: 1000230936-2015
• 负责人: Nikolov, Aleksandar
• 金额: $ 8.74万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=693064
• 关键词: Algorithms; Private; Data; Analysis

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.

该项目的重点是差异理论在计算机科学中的应用，其中私人数据分析和近似算法的设计是中心应用领域。离散理论研究的是在计算机上可以表示的离散结构在多大程度上可以近似于更易于分析的连续量。离散理论提出了迷人的计算问题，并与计算机科学的中心领域有联系，如组合优化，数据结构，以及私人数据分析领域，该领域开发了从数据中提取有用信息的方法，同时保护个人隐私。