  With the widespread application of machine learning technology in recent
years, the demand for training data has increased significantly, leading to the
emergence of research areas such as data trading. The work in this field is
still in the developmental stage. Different buyers have varying degrees of
demand for various types of data, and auctions play a role in such scenarios
due to their authenticity and fairness. Recent related work has proposed
combination auction mechanisms for different domains. However, such mechanisms
have not addressed the privacy concerns of buyers. In this paper, we design a
\textit{Data Trading Combination Auction Mechanism based on the exponential
mechanism} (DCAE) to protect buyers' bidding privacy from being leaked. We
apply the exponential mechanism to select the final settlement price for the
auction and generate a probability distribution based on the relationship
between the price and the revenue. In the experimental aspect, we consider the
selection of different mechanisms under two scenarios, and the experimental
results show that this method can ensure high auction revenue and protect
buyers' privacy from being violated.
