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：现代数据汇总往往采取从用户网络收集数据的平台形式。这些用户现在比以往更要求以隐私保障的方式保护他们提供的数据。这导致了对最佳数据获取框架的研究,其中最佳性标准通常是尽量扩大试图获取数据的代理商的效用。这涉及确定如何在不同隐私级别上向用户分配款项,用于购买其数据。本文件的主要目标是确定一个公平数额,在特定隐私级别上向用户支付数据。我们提出了一个与著名的Shapley价值相类似的公平原则定义。引入了两个公平概念。首先将平台和用户视为共同联盟的成员,并完整描述如何在平台和用户之间划分效用。在第二个概念中,公平仅由用户来界定,导致平台的潜在公平约束机制设计问题。我们考虑了涉及私人混杂数据的明确例子,并展示了这些公平概念是如何应用的。我们最了解的是,这些是明确考虑隐私制约的数据的第一个公平概念。