Travel: Student Travel Grant for 2022 Boston Differential Privacy Summer School

旅行：2022 年波士顿差异隐私暑期学校学生旅行补助金

• 批准号: 2227905
• 负责人: Adam Smith
• 金额: $ 1.5万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227905&HistoricalAwards=false
• 关键词: Travel; Student; Grant; 2022; Boston

The widespread use of sensitive personal information to train data-driven systems raises myriad challenges relating to privacy. One central question is the extent to which the outputs of a system—summary statistics, decisions, machine learning models, and more—reveal individual-level information. Differential privacy has emerged as a central concept in addressing this question. There is a broad and still growing body of research on differential privacy, as well as a number of deployed differentially private systems in industry and government.This award will provide travel grants for US-based students to attend the Differential Privacy summer school to be held in Boston, MA, June 6-10, 2022, partly co-located with the Symposium on the Foundations of Responsible Computing (FORC 2022), also held in Boston. The DP summer school will introduce students to a range of cutting-edge topics in the theory and applications of differential privacy. These include short courses on differentially private statistical inference, the issues arising in practical deployments at national scale, synthetic data generation, and algorithms for private processing of distributed data. There will also be guest lectures on connections between privacy and other fields, notably law and policy. Students will have the opportunity to work closely with each other, professionals in the field, and the faculty on the short-course topics via in-class interactions and problem-solving sessions. The summer school will provide a unique opportunity to graduate students to learn the state of the art in an important and rapidly developing field.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.

广泛使用敏感个人信息来训练数据驱动系统带来了与隐私相关的无数挑战，一个核心问题是系统的输出（汇总统计数据、决策、机器学习模型等）在多大程度上揭示了个人层面。差异隐私已成为解决这一问题的核心概念，关于差异隐私的研究越来越广泛，并且在行业和政府中部署了许多差异隐私系统。该奖项将提供差旅补助。供美国学生参加差别化课程隐私暑期学校将于 2022 年 6 月 6 日至 10 日在马萨诸塞州波士顿举行，部分时间与同样在波士顿举行的负责任计算基础研讨会 (FORC 2022) 同期举办。 DP 暑期学校将向学生介绍差异隐私理论和应用的一系列前沿主题，包括差异隐私统计推断、国家规模实际部署中出现的问题、合成数据生成以及隐私处理算法的短期课程。还将有关于隐私与其他领域、著名法律和政策之间的联系的客座讲座，学生将有机会通过短期课程主题与彼此、该领域的专业人士和教师密切合作。 - 课堂互动和解决问题的课程将为研究生提供一个独特的机会，让他们了解一个重要且快速发展的领域的最新技术。该奖项是 NSF 的法定使命，并通过使用评估被认为值得支持。基金会的智力价值和更广泛的影响审查标准。