An Evolutionary Game-Theoretic Trust Study of a Blockchain-Based Personal Health Data Sharing Framework

During the past few years, medical Internet-of-Things devices have experienced a massive growth and they are currently generating tremendous volumes of data every day, which are highly valuable as they can be of great importance to all stakeholders within the healthcare industry. In this context, many blockchain-based solutions have been proposed to mitigate the siloed burden in different healthcare systems which prevented achieving a patient-centric, transparent, and secure data sharing. However, when multiple independent entities try to share their data, trust is not necessarily guaranteed. The scope of this paper is to explore the impact of verification on the level of trust among the different entities of the health data-trading system by proposing an evolutionary game theoretic model. We also present the numerical analysis of the evolution of trust in terms of different game parameters adopted to evaluate their impact on eliminating malicious (i.e., untrustworthy) players from the system.