Machine Learning Model Trading with Verification under Information Asymmetry

TL;DR

This paper introduces a game-theoretic verification approach to address information asymmetry in ML model trading, analyzing strategic deception and optimal pricing to improve market transparency and outcomes.

Contribution

It proposes a verification-based framework for ML model trading, analyzing deception strategies and designing optimal pricing schemes under information asymmetry.

Findings

Verification reduces model deception probability

Reducing information asymmetry benefits both buyers and sellers

Protecting order information does not improve payoffs

Abstract

Machine learning (ML) model trading, known for its role in protecting data privacy, faces a major challenge: information asymmetry. This issue can lead to model deception, a problem that current literature has not fully solved, where the seller misrepresents model performance to earn more. We propose a game-theoretic approach, adding a verification step in the ML model market that lets buyers check model quality before buying. However, this method can be expensive and offers imperfect information, making it harder for buyers to decide. Our analysis reveals that a seller might probabilistically conduct model deception considering the chance of model verification. This deception probability decreases with the verification accuracy and increases with the verification cost. To maximize seller payoff, we further design optimal pricing schemes accounting for heterogeneous buyers' strategic behaviors. Interestingly, we find that reducing information asymmetry benefits both the seller and buyer. Meanwhile, protecting buyer order information doesn't improve the payoff for the buyer or the seller. These findings highlight the importance of reducing information asymmetry in ML model trading and open new directions for future research.