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.

数据驱动的决策是现代经济的引擎。 随着数据成为越来越重要的资源，了解其价值变得至关重要。 该项目将数据经济研究与不断发展的数据隐私领域结合起来，提出一个量化数据和隐私价值的框架。将探讨生成数据的用户与收集数据并从中受益的平台之间的关系，加深我们对公平和开放数据市场构成的理解。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，重点放在数据公平支付的概念上。 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、更高效的经济。此外，该项目还涉及公平的重要主题。这项研究有潜力通过研究一个基本框架来改变数据的查看、处理和货币化方式，在这个框架中，平台和个人都可以从数据价值中公平受益。该项目系统地解决了如何量化数据价值的基本问题。将数据放在以隐私为中心的博弈论框架中，以探索平台和用户与数据之间的关系，从而产生公平开放的数据市场的概念。使用 Shapley 值的基本博弈论概念来考虑数据公平支付的概念，并将该概念扩展为包括异构用户隐私的关键组成部分。具体来说，拟议的项目研究了如何量化提供隐私的成本、如何将各种异构隐私级别的数据价值货币化以及如何使平台能够设计公平的激励结构等技术问题。拟议的研究是高度跨学科的，包括优化、机器学习、概率论和统计学的元素，并且批判性地从经济学和博弈论的相关方面（例如纳什均衡和沙普利值）来对待公平和价值等概念。该项目旨在将统计推断和机器学习设置的严格隐私保证方面的最新进展与在异构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价值和更广泛的影响审查标准。