Rademics Research Institute

Abstract

The integration of homomorphic encryption (HE) within IoT-connected smart grid systems presents a promising solution for ensuring data privacy and security, particularly in environments where sensitive energy data was transmitted and processed. The computational overhead of HE has hindered its widespread adoption, especially in resource-constrained devices within the grid. This chapter explores the synergies between HE and emerging technologies, such as edge and fog computing, lightweight cryptography, and hardware acceleration, to enhance the efficiency and feasibility of real-time encrypted data processing in smart grids. A detailed analysis of computational challenges and optimization strategies was presented, focusing on reducing the latency and energy consumption associated with HE operations. Case studies and experimental evaluations highlight successful implementations of hardware-accelerated HE in smart grid applications, demonstrating significant improvements in system performance and scalability. The chapter also examines the comparative advantages of HE over traditional encryption techniques, emphasizing its potential for securing critical infrastructure while maintaining privacy in decentralized power networks. Overall, this work provides a comprehensive framework for overcoming the challenges of HE implementation in smart grids and paves the way for future advancements in cyber-resilient, privacy-preserving energy management systems.

Introduction

The integration of homomorphic encryption (HE) in IoT-connected smart grid systems represents a significant advancement in the realm of data security and privacy [1,2]. With the growing adoption of smart grids, which rely heavily on interconnected devices such as smart meters, sensors, and controllers, ensuring the confidentiality of sensitive energy data has become a critical concern. Traditional encryption techniques typically require decryption before data can be processed, which poses significant privacy risks [3]. Homomorphic encryption, allows computations to be performed directly on encrypted data, enabling privacy-preserving analytics without exposing the underlying sensitive information [4]. This capability was especially important in environments like smart grids, where data transmission occurs over potentially insecure networks [5]. HE faces challenges such as high computational complexity and increased resource demands, making its real-time application in smart grids a topic of considerable research [6].

As smart grid systems become increasingly complex, the need for robust security mechanisms grows, particularly when dealing with the vast amounts of data generated by IoT devices [7]. The real-time nature of smart grid operations further complicates this challenge. Secure and timely data processing was required to make decisions related to grid optimization, load balancing, demand response, and fault detection [8]. Homomorphic encryption, while offering unparalleled privacy protection, introduces significant computational overhead due to the complex arithmetic operations involved. This complexity limits its practical application, particularly in resource-constrained IoT devices embedded in the grid infrastructure [9,10]. Therefore, a key focus of ongoing research was to optimize HE to reduce its computational burden without compromising its security features [11].