Anonymity Maintenance Algorithm (Ama) Of Health Care Data from IoT Devices Using Edge System with Transfer Learning

Abstract

In order to fully leverage the potential of the Internet of Things (IoT), it is crucial to safeguard against potential threats and prioritize the security and privacy of data collected and extracted from IoT devices. However, the implementation of security and privacy technology within IoT presents a number of unique challenges. This is due to the fact that IoT solutions comprise a diverse array of security and privacy solutions that are designed to safeguard IoT data at the device, infrastructure/platform, and application layers. As a result, ensuring end-to-end privacy protection across these three layers is a significant challenge within IoT. IoT devices implemented with recent advancements utilize edge system and monitors fitness of a person privately. In our proposed concept anonymity maintenance done along with privacy preserving in query processing in accordance with health monitoring values. In this edge system transfer learning applied to transfer minimum data which is privacy preserved to equip edge system about IoT devices in spite of their location. De-duplication will be done in edge system in health values monitoring thus frequency learning will be inculcated to know the frequency of that device in specified location. In health-oriented processing of query the privileges will be granted for each category of people to access integrity of data according to their standard of authentication. While granting privileges concerned person can handle all their available personal data using their authentic login details. Best edge system will be chosen using PSO or GWO techniques thus de-duplication implemented efficiently.