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.

翻译：机器学习（ML）模型交易以其在保护数据隐私方面的作用而闻名，但面临一个主要挑战：信息不对称。这一问题可能导致模型欺诈，即卖方为获取更多收益而虚报模型性能，这是现有文献尚未完全解决的问题。我们提出了一种博弈论方法，在ML模型市场中增加一个验证步骤，使买方在购买前能够检查模型质量。然而，该方法可能成本高昂且提供的信息不完善，从而增加了买方的决策难度。我们的分析表明，考虑到模型验证的可能性，卖方可能会以一定概率实施模型欺诈。该欺诈概率随验证准确性的提高而降低，随验证成本的增加而上升。为了最大化卖方收益，我们进一步设计了考虑异质买方策略行为的最优定价方案。有趣的是，我们发现减少信息不对称对卖方和买方均有益处。同时，保护买方订单信息并不会提高买方或卖方的收益。这些发现凸显了在ML模型交易中减少信息不对称的重要性，并为未来研究开辟了新的方向。