Machine Learning Model Trading with Information Asymmetry

Xiang Li, Jianwei Huang, Kai Yang, Chenyou Fan

Abstract: Machine learning (ML) model trading prevents data breaches in privacy-sensitive data-driven applications. Departing from commonly assumed complete information scenarios, we consider the more practical trading scenario where model deception may emerge under information asymmetry. More specifically, the model seller may provide false information on model quality to maximize her payoff. This paper takes the first step in tackling information asymmetry through the lens of model verification. We propose an ML model market that allows buyers to verify model quality before purchasing. Such verification can be costly and often imperfect, which makes the buyer's decision highly nontrivial. We first formulate the ML model trading process as a three-stage sequential game with imperfect information, where the seller determines the model delivery strategy after observing the buyer's order decision. Our analysis reveals that at the equilibrium, the seller will probabilistically conduct model deception, considering the possibility of model verification. The equilibrium deception probability increases with the buyer's verification cost and decreases with verification accuracy. Interestingly, we also show that reducing information asymmetry through verification benefits both the buyer and seller. We further consider a second market model with buyer order information protection, where the buyer's order information is unobservable before the seller makes the delivery strategy. Our analysis shows a surprising result under this market model: protecting buyer's order information will not increase the payoff of either the buyer or seller.