# Data Trading Combination Auction Mechanism based on the Exponential Mechanism

arXiv: https://arxiv.org/abs/2405.07336 · HTML: https://mlsanity.com/paper/2405.07336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 12 May 2024.

## Builds on (most cited first, 5 of 5)

- [Model-based Pricing for Machine Learning in a Data Marketplace](https://mlsanity.com/paper/1805.11450/) (2018)
- [Benefits of Permutation-Equivariance in Auction Mechanisms](https://mlsanity.com/paper/2210.05579/) (2022)
- [Differentially Private Double Spectrum Auction with Approximate Social Welfare Maximization](https://mlsanity.com/paper/1810.07873/) (2018)
- [Differentially Private Combinatorial Cloud Auction](https://mlsanity.com/paper/2001.00694/) (2020)
- [Combinatorial Resources Auction in Decentralized Edge-Thing Systems Using Blockchain and Differential Privacy](https://mlsanity.com/paper/2108.05567/) (2021)

## Abstract (arXiv)

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 textitData 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.