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CRII: SaTC: Data Privacy for Strategic Agents

CRII：SaTC：战略代理的数据隐私

基本信息

批准号：
2147657
负责人：
Rachel Cummings
金额：
$ 17.5万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147657&HistoricalAwards=false
关键词：
CRII SaTC Data Privacy Strategic

项目摘要

This project lays the groundwork for understanding how existing tools for privacy-preserving data analysis interact with strategic and human aspects of practical privacy guarantees. When strategic individuals have privacy concerns about the use of their data, they may modify their behavior to ensure less, or perhaps more favorable, information is revealed. The project's novelties are an interdisciplinary approach, which combines tools from algorithm design, machine learning, and economics. The broader significance and importance of this work is to provide a critical step for society's ability to collect useful data and to interpret data via existing algorithms. As more personal data are collected, stored, and used in algorithmic decision making, these results are useful in the legal and policy landscape of personal data management. This work has two main technical thrusts. First, this project studies how privacy technologies can be designed and deployed to manage privacy concerns of strategic individuals. This yields insight into the design of optimal privacy technologies for strategic individuals in practical application areas. Second, this project develops data analysis techniques for settings where data are generated by privacy-aware individuals. This yields tools for the design and analysis of algorithms to efficiently learn and optimize from a strategic individual's data. This project also includes a significant educational and outreach component, including curriculum development, mentorship of students, and workshop organization.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.

该项目为理解现有的隐私保护数据分析工具如何与实际隐私保障的战略和人性化方面相互作用奠定了基础。当战略个人对数据的使用有隐私担忧时，他们可能会修改自己的行为，以确保透露更少或更有利的信息。该项目的新颖之处是一种跨学科的方法，它结合了算法设计，机器学习和经济学的工具。这项工作更广泛的意义和重要性是为社会收集有用数据和通过现有算法解释数据的能力提供关键一步。随着越来越多的个人数据被收集、存储和用于算法决策，这些结果在个人数据管理的法律的和政策环境中非常有用。这项工作有两个主要的技术要点。首先，该项目研究如何设计和部署隐私技术来管理战略个人的隐私问题。这将深入了解在实际应用领域的战略个人的最佳隐私技术的设计。其次，该项目开发了数据分析技术，用于由隐私意识强的个人生成数据的环境。这产生了用于设计和分析算法的工具，以有效地从战略个人的数据中学习和优化。该项目还包括一个重要的教育和推广组成部分，包括课程开发，指导学生，和研讨会的组织。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。