NSF-BSF: Collaborative Research: AF: Small: Algorithmic Performance through History Independence

NSF-BSF：协作研究：AF：小型：通过历史独立性实现算法性能

• 批准号: 2420942
• 负责人: Martin Farach-Colton
• 金额: $ 30万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2420942&HistoricalAwards=false
• 关键词: NSF; BSF; Collaborative; Research; AF

A data structure is said to be history independent (HI) if the way that the data is stored reveals nothing about the history of operations that lead to the current state. History independence was introduced to protect data structures from malicious attacks. For example, voting machines that store votes in the order they were cast can reveal surprising information about voters, whereas a history-independent data organization on a voting machine does not. This project considers a new use for history independence. The project will demonstrate that history independence can be a powerful analytical tool for designing randomized data structures and algorithms---especially in the case of oblivious adversaries. The project investigates problems in: (1) online algorithms, (2) resource allocation, (3) random-acess memory (RAM) and external-memory dictionary data structures, (4) load balancing, and (5) scalable concurrent data structures. The researchers have already shown how to use HI to crack a four-decades-old open problem in list labeling and to make substantial progress on a well-known load balancing problem. This work promises to broaden the scope of HI to both sequential and concurrent settings. The team will continue community-building efforts to introduce randomized algorithms to the undergraduate curriculum, as well as running workshops that can expose junior researchers to these techniques. The team will also continue its efforts to engage members of underrepresented groups in research earlier in their careers.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.

如果数据的存储方式没有揭示导致当前状态的操作的历史记录，则称该数据结构是历史独立的（HI）。 引入历史独立性是为了保护数据结构免受恶意攻击。 例如，按照投票顺序存储选票的投票机可以揭示有关选民的令人惊讶的信息，而投票机上独立于历史的数据组织则不能。 该项目考虑了历史独立性的新用途。 该项目将证明历史独立性可以成为设计随机数据结构和算法的强大分析工具——尤其是在对手不知情的情况下。 该项目研究以下方面的问题：(1) 在线算法，(2) 资源分配，(3) 随机存取存储器 (RAM) 和外部存储器字典数据结构，(4) 负载平衡，以及 (5) 可扩展并发数据结构。 研究人员已经展示了如何使用 HI 来解决列表标签中一个已有 40 年历史的开放性问题，并在一个众所周知的负载平衡问题上取得实质性进展。 这项工作有望将 HI 的范围扩大到顺序和并发设置。 该团队将继续社区建设工作，将随机算法引入本科课程，并举办研讨会，让初级研究人员接触这些技术。 该团队还将继续努力让代表性不足的群体的成员在其职业生涯的早期参与研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。