Permissioned Blockchain and Deep-Learning for Secure and Efficient Data Sharing in Industrial Healthcare Systems

Abstract

The industrial healthcare system has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate Permissioned Blockchain and smart contract with Deep Learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof) and validate the communicating entities using smart contract-based consensus mechanism. Second, the authenticated data is used to propose a novel DL scheme that combines Stacked Sparse Variational AutoEncoder (SSVAE) with Self-Attentionbased Bidirectional Long Short Term Memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format and SA-BiLSTM identifies and improves attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirms the superiority of PBDL framework over existing state-of-the-art techniques.