GuardHealth: Blockchain empowered secure data management and Graph Convolutional Network enabled anomaly detection in smart healthcare

• Published in: Journal of parallel and distributed computing Vol. 142; pp. 1 - 12
• Main Authors: Wang, Ziyu, Luo, Nanqing, Zhou, Pan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2020
• Subjects: Blockchain; Graph Convolutional Network; Security and privacy; Smart healthcare; Trust assessment; Graph Convolutional Network; Trust assessment; Security and privacy; Smart healthcare; Blockchain
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2020.03.004

The paradox between the dramatic development of medical data privacy demand and years of bureaucratic regulation has slowed innovation for electronic medical records (EMRs). We are at a historical point for such innovation to prompt patients data autonomy. In this paper, we propose GuardHealth: an efficient, secure and decentralized Blockchain system for data privacy preserving and sharing. GuardHealth manages confidentiality, authentication, data preserving and data sharing when handling sensitive information. We exploit consortium Blockchain and smart contract to achieve secure data storage and sharing, which prevents data sharing without permission. A trust model is utilized for precisely managing trust of users with the implementation of the state-of-art Graph Neural Network (GNN) for malicious node detection. Security analysis and experiment results show that the proposed scheme is applicable for smart healthcare system. •We propose a consortium Blockchain-based smart healthcare system for health data privacy preserving and sharing.•Using Proxy Re-encryption, user can dynamically allow requestors to access to data and revoke permissions easily at any time.•We design a trust assessment mechanism to improve the reliability of sharing data. Based on this, we conduct GCN to discriminate malicious nodes.