Abstract

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.

Introduction

The IOT has transformed the landscape of modern technology by interconnecting a vast array of devices, sensors, and systems [1,2]. This rapid expansion has led to unprecedented volumes of data generation, providing valuable insights and driving innovation across various sectors, including healthcare, smart cities, and industrial automation [3]. However, the proliferation of IoT devices also brings forth significant privacy and security challenges [4,5]. As these systems collect and transmit sensitive information, the need for robust privacy-preserving techniques becomes paramount to safeguard against unauthorized access and data breaches [6].

Privacy-preserving techniques in IoT encompass a range of strategies designed to protect sensitive data while ensuring its utility for analysis and decision-making [7]. These techniques include encryption algorithms, data masking, and anonymization methods, each aimed at mitigating the risk of data exposure [8]. The effectiveness of these techniques, however, was often constrained by the resource limitations of IoT devices [9]. Many IoT devices operate with restricted computational power, memory, and energy, which complicates the implementation of advanced privacy measures without degrading overall system performance [10].