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Data markets facilitate decentralized data exchange for applications such asprediction, learning, or inference. The design of these markets is challengedby varying privacy preferences as well as data similarity among data owners.Related works have often overlooked how data similarity impacts pricing anddata value through statistical information leakage. We demonstrate that datasimilarity and privacy preferences are integral to market design and propose aquery-response protocol using local differential privacy for a two-party dataacquisition mechanism. In our regression data market model, we analyzestrategic interactions between privacy-aware owners and the learner as aStackelberg game over the asked price and privacy factor. Finally, wenumerically evaluate how data similarity affects market participation andtraded data value.

Privacy-Aware Data Acquisition under Data Similarity in Regression Markets