In the digital age, data is a valuable commodity, and data marketplaces offer lucrative opportunities for data owners to monetize their private data. However, data privacy is a significant concern, and differential privacy has become a popular solution to address this issue. Private data trading systems (PDQS) facilitate the trade of private data by determining which data owners to purchase data from, the amount of privacy purchased, and providing specific aggregation statistics while protecting the privacy of data owners. However, existing PDQS with separated procurement and query processes are prone to over-perturbation of private data and lack trustworthiness. To address this issue, this paper proposes a framework for PDQS with an integrated procurement and query process to avoid excessive perturbation of private data. We also present two instances of this framework, one based on a greedy approach and another based on a neural network. Our experimental results show that both of our mechanisms outperformed the separately conducted procurement and query mechanism under the same budget regarding accuracy.

翻译：在数字时代，数据是一种宝贵的商品，数据市场为数据所有者提供了将其私有数据货币化的有利可图的机会。然而，数据隐私是一个重大问题，差分隐私已成为解决该问题的流行方案。私有数据交易系统(PDQS)通过确定向哪些数据所有者购买数据、购买隐私量以及在保护数据所有者隐私的同时提供特定的聚合统计信息，促进了私有数据的交易。然而，现有采用分离式采购与查询流程的PDQS容易导致私有数据的过度扰动并缺乏可信度。为解决此问题，本文提出了一种集成化采购与查询流程的PDQS框架，以避免对私有数据的过度扰动。我们还提出了该框架的两个实例，一个基于贪心方法，另一个基于神经网络。我们的实验结果表明，在相同预算下，我们提出的两种机制在准确性方面均优于分离式采购与查询机制。