Privacy Preserving Data Fusion Scheme for Heterogeneous Networks in IoT Devices

Abstract

Data Fusion at edge computing plays an important role in IoT infrastructure and a lot of research has already been carried out in this domain on privacy preservation of homogeneous data fusion. However, there still remains a dire need to design a secure and lightweight privacy preserving scheme for heterogeneous data fusion which should be dynamic and adaptive in nature that can address the issues with authentication of the devices connected to CSP for communication purposes and for fused data communication while providing privacy preservation. This research focused to take data from multiple sensors i.e. heterogeneous data and then use data fusion techniques to accurately identify the action needed to be taken autonomously by the underlying machine. The main objective of this study is to put forward a secure and efficient scheme to overcome these prevailing issues. This thesis has presented a novel PPFHI scheme for heterogeneous data fusion in IoT devices that can efficiently balance privacy and trust assessment while requiring little overhead in terms of computation, communication, and storage to enable distributed data fusion across the e-healthcare sector. Additionally, we have provided in-depth theoretical research, and the findings have shown that the PPFHI scheme is better compared to state-of-the-art schemes in many ways, including the accuracy of fusion outcomes.