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

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。 该项目将数据的经济研究与日益增长的数据隐私领域结合起来，提出了一个量化数据和隐私价值的框架。我们将探讨产生数据的用户与收集并从中受益的平台之间的关系，从而加深我们对公平开放的数据市场的理解。重点放在公平支付数据的概念，考虑从经济学的知名概念，并将其扩展到包括用户隐私的关键组成部分。 更好地理解数据和隐私的价值可以增强个人和监管机构的能力，从而实现更强大、更富有成效的经济。此外，该项目还涉及公平这一重要议题。通过研究一个平台和个人都能从数据价值中公平受益的基本框架，本研究有可能改变数据的看待、处理和货币化方式。本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，以探索平台和用户之间的数据关系，这就形成了一个公平和开放的数据市场的概念。公平支付数据的概念被认为是使用的基本博弈论的概念的Shapley值，并扩展这一概念，包括异构用户隐私的关键组成部分。具体而言，拟议的项目研究了如何量化提供隐私的成本，如何在各种不同的隐私级别上将数据的价值货币化，以及如何使平台能够设计公平的激励结构等技术问题。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，以及批判性地，从经济学和博弈论的相关方面，如纳什均衡和Shapley值来对待公平和价值等概念。该项目旨在将最近在统计推断和机器学习环境中严格的隐私保证方面取得的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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.