Differentially Private Machine Learning-powered Combinatorial Auction Design

TL;DR

This paper introduces a differentially private machine learning approach to combinatorial auctions that guarantees truthfulness and high social welfare, leveraging privacy techniques like the Exponential Mechanism.

Contribution

It develops a novel auction mechanism that ensures truthfulness and efficiency using differential privacy principles, a significant advancement over prior non-private auction designs.

Findings

The mechanism guarantees truthfulness in combinatorial auctions.

It preserves high social welfare similar to non-private auctions.

The approach is effective in both asymptotic and non-asymptotic regimes.

Abstract

We present a new approach to machine learning-powered combinatorial auctions, which is based on the principles of Differential Privacy. Our methodology guarantees that the auction mechanism is truthful, meaning that rational bidders have the incentive to reveal their true valuation functions. We achieve this by inducing truthfulness in the auction dynamics, ensuring that bidders consistently provide accurate information about their valuation functions. Our method not only ensures truthfulness but also preserves the efficiency of the original auction. This means that if the initial auction outputs an allocation with high social welfare, our modified truthful version of the auction will also achieve high social welfare. We use techniques from Differential Privacy, such as the Exponential Mechanism, to achieve these results. Additionally, we examine the application of differential privacy in auctions across both asymptotic and non-asymptotic regimes.