EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Econometrically Inferring and Using Individual Privacy Preferences

EAGER：SaTC：早期跨学科合作：计量经济学推断和使用个人隐私偏好

基本信息

批准号：
1915813
负责人：
Denis Nekipelov
金额：
$ 29.96万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915813&HistoricalAwards=false
关键词：
EAGER SaTC Early Stage Interdisciplinary

项目摘要

Many online platforms use economic mechanisms to estimate the best ways to match consumers and businesses with products and services. Effective matches may require using personal consumer data but doing so may intrude on consumers' privacy. This project will use formal concepts of privacy to analyze the use of personal information in mechanism design. The goal is to develop tools for understanding the value and cost of collecting and using personal data, and provide mechanisms that allow designers to build systems that make meaningful and well understood tradeoffs between utility and privacy.The project combines research on mechanism design and econometrics to provide a new perspective on privacy. The project will develop methods that use ideas from econometrics to reveal concrete privacy preferences for individuals and aggregate distributions, and connect those preferences to formal privacy models, including differential privacy. The revealed privacy preferences for individuals, or aggregate for distributions, can then be used to design mechanisms with concrete and meaningful privacy and utility tradeoffs based on users' individual privacy preferences. The broader goal is to transform abstract privacy guarantees into concrete tools for incorporating privacy preferences to maximize consumer utility as well as business decisions.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.

许多在线平台使用经济机制来估算将消费者和企业与产品和服务相匹配的最佳方法。有效的匹配可能需要使用个人消费者数据，但是这样做可能会侵入消费者的隐私。该项目将使用正式的隐私概念来分析机制设计中个人信息的使用。目的是开发工具，以理解收集和使用个人数据的价值和成本，并提供使设计人员能够在实用性和隐私之间建立有意义且富有理解的权衡的系统。该项目结合了关于机制设计和计量经济学的研究，以提供有关隐私的新观点。该项目将开发使用计量经济学的想法来揭示个人和汇总分布的具体隐私偏好的方法，并将这些偏好与正式隐私模型（包括差异隐私）联系起来。然后，根据用户的个人隐私偏好，可以使用针对个人的揭示隐私偏好，或用于分布的汇总偏好，以设计具有具体和有意义的隐私和公用事业权衡的机制。更广泛的目标是将抽象隐私保证转换为具体工具，以纳入隐私偏好，以最大化消费者实用程序和业务决策。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。