  Data trading has been hindered by privacy concerns associated with user-owned
data and the infinite reproducibility of data, making it challenging for data
owners to retain exclusive rights over their data once it has been disclosed.
Traditional data pricing models relied on uniform pricing or subscription-based
models. However, with the development of Privacy-Preserving Computing
techniques, the market can now protect the privacy and complete transactions
using progressively disclosed information, which creates a technical foundation
for generating greater social welfare through data usage. In this study, we
propose a novel approach to modeling multi-round data trading with
progressively disclosed information using a matchmaking-based Markov Decision
Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism
(SWDPM) to find optimal pricing strategies. To the best of our knowledge, this
is the first study to model multi-round data trading with progressively
disclosed information. Numerical experiments demonstrate that the SWDPM can
increase social welfare 3 times by up to 54\% in trading feasibility, 43\% in
trading efficiency, and 25\% in trading fairness by encouraging better matching
of demand and price negotiation among traders.
