Data trading is increasingly gaining attention. However, the inherent replicability and privacy concerns of data make it challenging to directly apply traditional trading theories to data markets. This paper compares data trading markets with traditional ones, focusing particularly on how the replicability and privacy of data impact data markets. We discuss how data's replicability fundamentally alters the concept of opportunity cost in traditional microeconomics within the context of data markets. Additionally, we explore how to leverage this change to maximize benefits without compromising data privacy. This paper outlines the constraints for data circulation within the privacy domain chain and presents a model that maximizes data's value under these constraints. Specific application scenarios are provided, and experiments demonstrate the solvability of this model.

翻译：数据交易日益受到关注。然而，数据固有的可复制性和隐私问题使得传统交易理论难以直接应用于数据市场。本文通过对比数据交易市场与传统市场，重点分析数据的可复制性和隐私性如何影响数据市场。我们探讨数据的可复制性如何从根本上改变传统微观经济学中机会成本概念在数据市场的应用，并研究如何利用这种变化在保护数据隐私的前提下实现收益最大化。本文提出隐私域链中数据流通的约束条件，并构建在约束条件下实现数据价值最大化的模型。通过具体应用场景的验证和实验，证明该模型具有可解性。