  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.
