Adaptive encryption scheme for IoT sensors network

Abstract

Artificial Intelligence (AI) and the Internet of Things (IoT) have revolutionised the way we live and work, bringing unpredictable levels of automation and decision-making. As a result, industries such as healthcare, finance, and smart cities have experienced significant changes. These technologies have transformed our lives to be more efficient, convenient, and connected. However, the rapid advancement of AI and IoT has also raised some concerns. Data privacy and security have become a major challenge with these systems processing massive amounts of sensitive personal and organisational information data. Highlighting the importance of implementing robust protection methods. This dissertation focuses on the different techniques used to maintain data privacy in AI and IoT ecosystems using privacy-preserving technologies (PETs), such as differential privacy (DP), federated learning (FL), and secure computation. These technologies are essential for compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Moreover, it is important to educate users about the associated risks of using AI and IoT and to encourage responsible behaviours. The core focus of this research is a dual-layer encryption schema that helps to protect sensitive data in IoT sensor networks by classifying the data as low and high-critical.