With the prevalence of data-driven business, data markets where data can be commoditized, circulated, and ex-ploited are gaining considerable interest in the data management community. However, the uncertainty in data value poses great challenges for data pricing and thus data trading, which is magnified by the externality arising from the replicable nature of data. In this paper, we present the first bargaining-based data market framework to resolve the externality in data markets. Gearing toward raw data trading, we propose a three-stage bar-gaining model to formulate trading dynamics, which ascertains the data price agreed by both sellers and buyers. With parameters instantiated in preparation stage, an iterative bidding algorithm with provable convergence is designed in negotiation stage to solve the data pricing problem by eliciting equilibrium bids from participants with their profits optimized. Approximation algorithms with guaranteed bounds are presented in settlement stage to solve the NP-hard data allocation problem for profit maximization for the data seller with individual rationality satisfied for data buyers. Experiments on real datasets verify the effectiveness and efficiency of our framework.

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.

Driven by the growing field of data intelligence, data market emerges as a promising paradigm for data exchange, enabling the full utilization of data. Data pricing is an essential function in data market that reflects the values or cost of data and is dependent on interactions among multiple market participants including data buyers, data sellers, and data brokers. Game theory presents a promising approach to model the multi-participant interplay in data pricing, yet challenged by the specific nature of data. In this paper, we present a blueprint for applying game theory to data pricing. From a game-theoretic perspective, we highlight the unique characteristics of data (compared to traditional goods) and suggest important desiderata for effective data pricing. We identify four key dimensions (Participant, Object, Action, and Information) to understand the landscape of game theory based data pricing. Within each dimension, data-specific challenges and research gaps are identified. Our work establishes a foundational understanding of data pricing through the lens of game theory and opens up promising research directions in this developing field.