Dealer: End-to-End Data Marketplace with Model-based Pricing

TL;DR

This paper introduces Dealer, an end-to-end data marketplace with model-based pricing that incentivizes data owners to contribute data by valuing their contributions through Shapley values and setting privacy-aware prices to maximize revenue.

Contribution

It proposes a formal data market framework with a Shapley value-based contribution valuation and privacy-aware pricing, addressing data restrictions and maximizing revenue.

Findings

DP-Dealer satisfies formal properties and guarantees.

The mechanism effectively incentivizes data contribution.

Experimental results show efficiency and effectiveness.

Abstract

Data-driven machine learning (ML) has witnessed great successes across a variety of application domains. Since ML model training are crucially relied on a large amount of data, there is a growing demand for high quality data to be collected for ML model training. However, from data owners' perspective, it is risky for them to contribute their data. To incentivize data contribution, it would be ideal that their data would be used under their preset restrictions and they get paid for their data contribution. In this paper, we take a formal data market perspective and propose the first en\textbf{\underline{D}}-to-\textbf{\underline{e}}nd d\textbf{\underline{a}}ta marketp\textbf{\underline{l}}ace with mod\textbf{\underline{e}}l-based p\textbf{\underline{r}}icing (\emph{Dealer}) towards answering the question: \emph{How can the broker assign value to data owners based on their contribution to the models to incentivize more data contribution, and determine pricing for a series of models for various model buyers to maximize the revenue with arbitrage-free guarantee}. For the former, we introduce a Shapley value-based mechanism to quantify each data owner's value towards all the models trained out of the contributed data. For the latter, we design a pricing mechanism based on models' privacy parameters to maximize the revenue. More importantly, we study how the data owners' data usage restrictions affect market design, which is a striking difference of our approach with the existing methods. Furthermore, we show a concrete realization DP-\emph{Dealer} which provably satisfies the desired formal properties. Extensive experiments show that DP-\emph{Dealer} is efficient and effective.