Modern data aggregation often takes the form of a platform collecting data from a network of users. More than ever, these users are now requesting that the data they provide is protected with a guarantee of privacy. This has led to the study of optimal data acquisition frameworks, where the optimality criterion is typically the maximization of utility for the agent trying to acquire the data. This involves determining how to allocate payments to users for the purchase of their data at various privacy levels. The main goal of this paper is to characterize a fair amount to pay users for their data at a given privacy level. We propose an axiomatic definition of fairness, analogous to the celebrated Shapley value. Two concepts for fairness are introduced. The first treats the platform and users as members of a common coalition and provides a complete description of how to divide the utility among the platform and users. In the second concept, fairness is defined only among users, leading to a potential fairness-constrained mechanism design problem for the platform. We consider explicit examples involving private heterogeneous data and show how these notions of fairness can be applied. To the best of our knowledge, these are the first fairness concepts for data that explicitly consider privacy constraints.

翻译：现代数据聚合常表现为平台从用户网络中收集数据。如今，用户越来越多地要求其提供的数据受到隐私保护的保障。这促使研究者探索最优数据获取框架，其中最优性准则通常为试图获取数据的代理方效用最大化。这涉及确定如何向用户分配支付，以在不同隐私级别购买其数据。本文的主要目标是刻画在给定隐私水平下应向用户支付的数据公平金额。我们提出了一种公理化的公平性定义，类似于著名的沙普利值。本文引入了两种公平性概念。第一种将平台和用户视为共同联盟的成员，完整描述了如何在平台与用户之间分配效用。第二种概念仅对用户之间定义公平性，从而为平台衍生出潜在的公平性约束机制设计问题。我们通过涉及异质性私有数据的显式案例，展示了这些公平性概念的应用方式。据我们所知，这是首个明确考虑隐私约束的数据公平性概念。