Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences as well as data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value.

翻译：数据市场促进了预测、学习或推理等应用中分散数据的交换。这些市场的设计面临不同隐私偏好以及数据所有者之间数据相似性的挑战。相关研究常常忽略了数据相似性如何通过统计信息泄露影响定价和数据价值。我们证明了数据相似性和隐私偏好是市场设计的核心要素，并提出了一种基于本地差分隐私的查询-响应协议，用于两方数据获取机制。在我们的回归数据市场模型中，我们将隐私感知所有者与学习者之间的战略互动分析为关于询问价格和隐私因子的Stackelberg博弈。最后，我们通过数值实验评估了数据相似性如何影响市场参与和交易数据价值。