Data Trading Combination Auction Mechanism based on the Exponential Mechanism

TL;DR

This paper introduces a privacy-preserving data trading auction mechanism using the exponential mechanism, ensuring high revenue and privacy protection for buyers in data markets.

Contribution

It proposes a novel combination auction mechanism based on the exponential mechanism that addresses privacy concerns in data trading auctions.

Findings

Ensures high auction revenue

Protects buyers' bidding privacy

Effective under different scenarios

Abstract

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.