  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.
