EAGER: SaTC: Quantifying the Fair Value of Data and Privacy in Distributed Learning

EAGER：SaTC：量化分布式学习中数据和隐私的公允价值

• 批准号: 2232146
• 负责人: Kannan Ramchandran
• 金额: $ 30万
• 依托单位: University of California-Berkeley
• 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: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232146&HistoricalAwards=false
• 关键词: EAGER; SaTC; Quantifying; Fair; Value

Data-driven decision-making drives the engine of our modern economy. As data becomes an increasingly important resource, understanding its value becomes critical. This project marries the economic study of data with the growing field of data privacy to present a framework for quantifying the value of data and privacy. The relationship between users who generate data and platforms that collect and benefit from this data will be explored, progressing our understanding of what constitutes a fair and open data market. Emphasis is placed on the concept of fair payment for data by considering well-known concepts from economics and extending them to include the critical component of user privacy. A better understanding of the value of data and privacy can empower individuals and regulators, leading to a stronger and more productive economy. In addition, the project addresses the important topic of fairness. This research has the potential to transform the way data is viewed, treated, and monetized by studying a fundamental framework where platforms and individuals can both fairly benefit from the value of data.This project systematically approaches the fundamental question of how to quantify the value of data in a privacy-centric game-theoretic framework in order to explore the relationship between platforms and users with data, leading to the concept of a fair and open data market. The concept of fair payment for data is considered using the foundational game-theoretic concept of the Shapley value, and extending this concept to include the critical component of heterogeneous user privacy. Specifically, the proposed project investigates the technical questions of how to quantify the cost of providing privacy, how to monetize the value of data at various heterogeneous levels of privacy, and how to enable platforms to design fair incentive structures. The proposed investigation is highly interdisciplinary, including elements of optimization, machine learning, probability theory, and statistics, as well as critically, from relevant aspects of economics and game theory such as Nash equilibria and Shapley value to treat concepts like fairness and value. The project aims to bridge the recent advancements in rigorous privacy guarantees for statistical inference and machine learning settings with the economics of quantifying the value of data under heterogeneous privacy requirements.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.

数据驱动的决策驱动着我们现代经济的引擎。随着数据成为一种日益重要的资源，了解其价值变得至关重要。该项目将数据的经济研究与日益增长的数据隐私领域结合在一起，为量化数据和隐私的价值提供了一个框架。将探讨产生数据的用户与收集这些数据并从中受益的平台之间的关系，从而促进我们对什么是公平和开放的数据市场的理解。重点是通过考虑经济学中的众所周知的概念并将其扩展到包括用户隐私的关键组成部分来强调公平支付数据的概念。更好地理解数据和隐私的价值可以赋予个人和监管机构权力，从而带来更强大和更具生产力的经济。此外，该项目还涉及公平这一重要话题。这项研究通过研究一个基本框架，使平台和个人都可以从数据的价值中公平受益，从而有可能改变数据的查看、处理和货币化的方式。该项目系统地探讨了如何在以隐私为中心的博弈论框架中量化数据价值的基本问题，以探索平台和用户与数据之间的关系，从而产生公平和开放的数据市场的概念。使用基本的博弈论概念Shapley值来考虑数据公平支付的概念，并将该概念扩展到包括不同用户隐私的关键组成部分。具体地说，拟议的项目调查了如何量化提供隐私的成本，如何将各种不同隐私级别的数据的价值货币化，以及如何使平台能够设计公平的激励结构。拟议的调查是高度跨学科的，包括优化、机器学习、概率论和统计学的元素，以及关键的是，从经济学和博弈论的相关方面，如纳什均衡和沙普利值，来处理公平和价值等概念。该项目旨在将统计推理和机器学习环境的严格隐私保障方面的最新进展与在不同隐私要求下量化数据价值的经济学联系起来。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。