Journal
COMPUTERS & SECURITY
Volume 108, Issue -, Pages -Publisher
ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.cose.2021.102353
Keywords
Smart healthcare system; Access control; Edge computing; Edge computing layer; Privacy -preserving searchable
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With the increasing sensitivity of medical data, the importance of a Smart Healthcare System is emphasized. The edge computing architecture shows great potential in protecting patient data privacy and reducing response time.
Nowadays, the healthcare system faces new challenges due to the increasing growth of sensitive patient data. There is a need for a Smart Healthcare System (SHS) that makes the healthcare system smarter to protect the data and maintain privacy. An SHS contains several IoT devices, sensors, and mobile nodes that collect the data from the patient's body and store it remotely. The most commonly used approach for storing data in the SHS is cloud computing-based storage. This type of system is very popular and provides common solutions to all users because it allows accessing the data remotely. The security and privacy of remotely located data is another important concem because the solution is available to all types of users. Remotely located data enables a lack of control over the data. The real-time health monitoring system based on cloud computing is not feasible because the response time is comparatively high. It is essential to provide some solution that reduces this response time. The edge computing approach reduces the response time in real-time cases as well as needs low bandwidth. This paper proposed a smart healthcare system based on edge computing architecture. This architecture consists of an intermediary layer called an edge computing layer responsible for maintaining the network latency and preserving the privacy of the patient data. This edge computing layer has handled the encryption and privacy of the patient data with the help of the Privacy-Preserving Searchable Encryption (PPSE) technique. The access control mech-anism is also implemented to restrict unauthorized access to the remote stored patient data. The proposed model's implementation, performance, and security analysis show high security, low latency, low transfer time, low power, and low energy compared to a similar approach. The computed results demonstrated transfer time (64.24%), power consumption (69.03%), and energy consumption (69.56%) is reduced using the edge computing approach. (c) 2021 Elsevier Ltd. All rights reserved.
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