Share: Stackelberg-Nash based Data Markets

With the prevalence of data-driven intelligence, data markets with various data products are gaining considerable interest as a promising paradigm for commoditizing data and facilitating data flow. In this paper, we present Stackelberg-Nash based Data Markets (Share) to first realize a demand-driven incentivized data market with absolute pricing. We propose a three-stage Stackelberg-Nash game to model trading dynamics which not only optimizes the profits of all selfish participants but also adapts to the common buyer-broker-sellers market flow and solves the seller selection problem based on sellers' inner competition. We define Stackelberg-Nash Equilibrium and use backward induction to solve the equilibrium. For inner Nash equilibrium, we apply the conventional direct derivation approach and propose a novel mean-field based method along with provable approximation guarantees for complicated cases where direct derivation fails. Experiments on real datasets verify the effectiveness and efficiency of Share.