DeepChain: A Deep Learning and Blockchain Based Framework for Detecting Risky Transactions on HIE System

Nowadays, Healthcare Information Exchange (HIE) plays a vital role in healthcare systems; it allows healthcare providers to access and share patient medical data electronically and securely. Subsequently, HIE eliminates redundant or unnecessary testing, and improves public health reporting and monitoring. Security is a very important challenge in the HIE systems since data are exchanged between different healthcare facilities (HCF), thus, the data are subject to be modified or altered. Hence, detecting modified or risky transactions is becoming a fundamental operation in HIE systems. In this paper, we propose a secure framework that combines between deep learning and blockchain, called as DeepChain, for detecting risky transactions in HIE systems. On one hand, DeepChain uses two types of blockchain to enhance the data security: a blockchain to store ordinary patient data, and an off-chain to store the sensitive patient's data. On the other hand, DeepChain uses an advanced deep learning model called generative adversarial network (GAN) with two-folds: first, it enhances the training phase of the model by generating additional synthetic health data, then it uses a discriminator to accurately detect the risky transactions in the testing phases. We evaluated the performance of our framework based on real health data while the obtained results shows the efficiency of DeepChain in detecting risky transactions and enhancing HIE security.