Privacy-Aware Data Acquisition under Data Similarity in Regression Markets (2312.02611v2)

Abstract: 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.

• The paper proposes a novel query-response protocol with local differential privacy to manage data exchange in regression markets.
• It models market interactions as a Stackelberg game where the learner sets prices and data owners decide participation based on privacy and similarity.
• Numerical simulations show that strategic pricing considering data similarity enhances market efficiency and fairness.

In the burgeoning world of data markets, privacy and data similarity are significant factors that affect how data is valued and exchanged. A recently published paper focuses on the delicate balance between privacy concerns and the value of data in regression data markets, where companies or individuals buy and sell data for applications like prediction, learning, or inference.

In a regression data market, parties engage in a system where data owners supply features that are used to predict a target variable. A central agent or 'learner' solicits these features from other participants and aims to build a regression model. However, different participants have different levels of willingness to share their data, driven by their privacy preferences. Additionally, the value of the data they offer can be impacted by data similarity – how similar one party's data is to another's.

The paper proposes a mechanism design where a query-response protocol, underpinned by local differential privacy (LDP), manages the exchange of data between the learner and the data owners, modeled as a two-party interaction. LDP is a statistical technique that ensures an individual's data cannot be re-identified in the released aggregate data. It works by introducing noise to the data, balancing privacy with the accuracy of the aggregate information.

The interactions in the market are modeled as a Stackelberg game, a strategic game theory model where one leader (the learner) and followers (data owners) interact. In such a framework, the learner proposes a price for the data, taking into consideration privacy factors, and the owners respond with their participation based on their privacy preferences and perceived value of their data.

The researchers conducted numerical simulations to demonstrate how the proposed mechanism works in practice. They found that the similarity among data owners' features significantly affects how much they participate in the market and the value of the traded data. Their results suggest that when data privacy is handled wisely, with strategic pricing linked to data owners' preferences, the efficiency of the regression data market improves, ensuring a fairer and privacy-aware trade-off between data utility and privacy.

In summary, the paper shines a light on the complexities of operating regression data markets in the presence of varying privacy preferences and data similarities. By introducing a novel privacy-aware data acquisition mechanism employing LDP, the paper makes significant strides in addressing these challenges, paving the way for more fair, efficient, and privacy-respecting data trade practices.