Data Pricing for Blockchain-based Car Sharing: A Stackelberg Game Approach

斯塔克伯格竞赛 计算机科学 数据共享 服务提供商 博弈论 数据建模 人气 原始数据 服务(商务) 运筹学 微观经济学 业务 数据库 经济 营销 病理 工程类 程序设计语言 社会心理学 替代医学 医学 心理学
作者
Chengzhen Xu,Kun Zhu,Changyan Yi,Ran Wang
标识
DOI:10.1109/globecom42002.2020.9322221
摘要

With the increasing popularity of car sharing, a large amount of vehicle data has been generated which has great potential values for various applications (e.g., analyzing user habits for more economic benefits). These valuable data can be traded among owners and buyers on a data trading platform. Traditionally, data is traded in a centralized market which requires data exchange by trustworthy authorities. In this work, to address the potential unreliable issues (e.g., data loss and leakage), we design a consortium blockchain-based data trading framework to create a P2P trading market and enhance the security of data trading. We classify the data into five types to distinguish data with different values. Specifically, we investigate the pricing issue in the proposed car-sharing data market, which consists of data owner, service provider and data buyer. The data owner gives the pricing strategy of original data, and then the service provider processes the raw data and provides hierarchical quality of data with different data accuracy and privacy levels to the buyer who determines the data purchase strategy. Based on the interactions among these three parties, we formulate the problem as a three-layer Stackelberg game. Backward induction is applied to analyze the solution of the problem, and we conduct theoretical analysis to show the existence of Stackelberg game equilibrium. Numerical results evaluate the performance of our system under different settings.
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