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

【Author】 Kumar, Randhir; Kumar, Prabhat; Tripathi, Rakesh; Gupta, Govind P.; Islam, A. K. M. Najmul; Shorfuzzaman, Mohammad

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】The industrial healthcaresystem 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 the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based 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 the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.

【Keywords】Medical services; Blockchains; Data privacy; Security; Cloud computing; Temperature sensors; Real-time systems; Blockchain; deep learning (DL); healthcare systems; Industrial Internet of Things (IIoT); intrusion detection system (IDS); privacy preservation

【发表时间】2022 NOV

【收录时间】2022-10-17

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-医疗领域