Ensuring Privacy and Security in IoT Data Analytics
S SASIKALA, P ECINTHA · 2024
As the IOT continues to expand, ensuring robust privacy and security in data analytics becomes increasingly critical. This book chapter delves into advanced privacy-preserving techniques specifically designed for large-scale IoT systems. It provides a comprehensive analysis of the scalability challenges associated with implementing these techniques, including encryption algorithms, and explores resource-aware solutions tailored to the constraints of IoT devices. The chapter further investigates quantitative methods for evaluating the trade-offs between privacy protection and system performance, offering insights into optimizing privacy measures without compromising operational efficiency. Emerging trends in privacy research are also addressed, highlighting future directions such as the integration of artificial intelligence, blockchain technology, and quantum computing. This analysis aims to bridge the gap between evolving privacy needs and the practical limitations of IoT systems, providing valuable guidance for researchers and practitioners in the field.