A Robust Framework for Ensuring Data Confidentiality and Security in Modern Healthcare Networks
Keywords:
Data security in healthcare, homomorphic-encryption, differential-privacy, federated-learningAbstract
The potential implementation of extensive data sharing in dispersed network systems is likely to give rise to concerns surrounding privacy, secrecy, and authentication inside the realm of cyberspace. The main objective of this work is to safeguard the privacy and confidentiality of data inside an unsecured environment during the exchange of audiovisual content between two Internet of Things (IoT) nodes. To effectively counteract an adversary and guarantee the confidentiality of data, we suggest the implementation of a resilient multi-level security strategy that relies on the principles of information hiding and chaos theory. While certain block-based resilient data concealing strategies based on the transform domain have demonstrated favorable outcomes, their suboptimal block and coefficient selection processes lead to inadequate performance against prevalent cyber-attacks. Therefore, we propose a Robust Framework for Ensuring Data Confidentiality and Security in EHR-based networks using federated-learning with homomorphic-encryption. A differential-privacy technique is used here to increase privacy, which entails adding noise to the aggregated model update. The suggested model outperforms the most recent techniques for data security and secrecy in networks based on electronic health records.
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