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.

翻译：数据交易因用户所拥有数据的隐私问题以及数据的无限可复制性而备受阻碍，使得数据所有者在其数据被披露后难以保留对数据的专有权利。传统数据定价模型依赖于统一定价或基于订阅的模型。然而，随着隐私保护计算技术的发展，市场现在能够在逐步披露信息的过程中保护隐私并完成交易，这为通过数据使用创造更大社会福利奠定了技术基础。在本研究中，我们提出了一种新颖的方法，利用基于匹配的马尔可夫决策过程对逐步披露信息下的多轮数据交易进行建模，并引入了一种社会福利优化的数据定价机制（SWDPM）以寻找最优定价策略。据我们所知，这是首个对逐步披露信息下多轮数据交易进行建模的研究。数值实验表明，SWDPM通过优化交易者之间的需求匹配与价格协商，可将社会福利提升高达3倍，其中交易可行性提升54%、交易效率提升43%、交易公平性提升25%。