  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.
