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.

数据驱动的决策推动了我们现代经济的引擎。 随着数据成为越来越重要的资源，理解其价值变得至关重要。 该项目将数据的经济研究与越来越多的数据隐私领域结合在一起，以提出一个量化数据和隐私价值的框架。将探讨生成数据和受益的数据的用户之间的关系，从而探索我们对公平和开放数据市场的理解。通过考虑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，重点放在数据公平支付的概念上。 更好地了解数据和隐私的价值可以增强个人和监管机构的能力，从而导致更强大，更有生产力的经济。此外，该项目还解决了公平的重要主题。这项研究具有通过研究一个基本框架来查看，处理和获利数据的潜力，在该框架中，平台和个人都可以从数据的价值中相当受益。本项目系统地探讨了如何量化数据在隐私中心的游戏框架中的基本问题，以探索平台与数据之间的关系，并探索数据和数据之间的概念，该概念是一个公平的概念，该概念是一个公平的概念。使用Shapley值的基础游戏理论概念考虑了数据的公平支付概念，并将此概念扩展到包括异质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成本的技术问题，如何在各种异质级别的隐私层面上获利数据价值，以及如何使平台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，概率理论和统计学的要素，以及从经济学和游戏理论的相关方面（例如纳什均衡和莎普利价值）来处理诸如公平和价值之类的概念。该项目旨在弥合严格隐私的最新进展，以保证统计推断和机器学习环境，具有量化在异质隐私要求下数据价值的经济学。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智力和更广泛影响的评估来进行评估的支持，这是值得的。