Privacy-Aware Data Acquisition under Data Similarity in Regression Markets

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. A. Context and Motivation In recent years, there has been a surge in Internet of Things (IoT) devices with sensing and computing capabilities, leading to an abundance of IoT data. Shashi Raj Pandey and Petar Popovski are with the Connectivity Section, Department of Electronic Systems, Aalborg University, Denmark. Pierre Pinson has primary affiliation with Dyson School of Design Engineering, Imperial College London, UK. He is also affiliated to the Technical University of Denmark, Department of Technology, Management and Economics, as well as with Halfspace This work was supported by the Villum Investigator Grant "WATER" from the Velux Foundation, Denmark.