CLOUD- SERVER BASED DATA PRIVACY USING CENTRALIZED AND FEDERATED LEARNING METHODS FOR EHR FRAMEWORK TO SECURE PATIENT DATA

Abstract

The effect of insider attack on the e- Healthcare system can lead to false examination of patient health records which have led to unaccountability of data usage and high financial cost as a result of data breaches in the e-healthcare without a highly efficient detection approach. A number of health centers have been faced with legal and reputational consequences as a result. This therefore requires the proposition of an efficient technique that can make this problem addressed most especially eHealth systems on the cloud environment as operations are currently operating with cloud services. Until such approaches are proposed, health records could be attacked and peradventure lead to poor treatment of patients due to misinformation and hence causing the death of individuals. This need serves as a key motivation for this research. In this, we proposed a new framework for detecting insider attacks in Cloud-based Healthcare system using watermarking extraction and logging detection technique. The approach gave an output of the number of activities performed by users with the permission update of legal and illegal intrusion into the system using an audit trail. The proposed approach executed with higher level of precision, recall and accuracy which makes it performs the excellent results.