SECURE DECISION SUPPORT SYSTEM IN MEDICAL CYBER PHYSICAL NETWORK

Abstract

Medical Secure Systems (MSSs) represent a fusion of computational and physical processes, posing significant challenges in both theory and application. This study aims to delve into this burgeoning multidisciplinary approach, particularly focusing on MSS within medical contexts, commonly referred to as Medical Secure Systems (MSS). Within MSS, the transmission of diverse data to either private or public cloud platforms for storage and analysis is commonplace. Leveraging machine learning algorithms, these data sets can be processed to facilitate informed decision-making by healthcare professionals. However, the sensitivity of such data, coupled with its exposure to third-party storage spaces, underscores the criticality of security measures. To address these concerns, this paper employs cryptographic techniques, notably AES encryption, to safeguard data before its storage on cloud servers. Moreover, an additional layer of security is introduced through the implementation of digital envelopes. Here, the AES encryption key is further encrypted using ECC encryption, bolstering the overall security framework. Furthermore, to streamline key management, the system utilizes a Key Distribution Center (KDC), tasked with generating and managing keys for all users. Experimental findings validate the efficacy of this MSS system, affirming its superior security posture compared to previous iterations while also minimizing key management overhead.