Secure and privacy-preserving smart metering systems: a novel framework for data aggregation using encryption and reversible watermarking

As the world embraces the new era of smart technologies and the proliferation of IoT equipment, such as smart grids and metering systems, a significant concern regarding the privacy and security of users' confidential information is rapidly emerging. Smart meters, serving as the primary source of energy consumption data in smart grid networks, can record energy usage with fine granularity. This continued use of smart meters enhances interaction between energy suppliers and consumers. However, the detailed information generated about the energy consumption patterns increases the risks of data breaches, identity theft, and other forms of cyberattacks. The remarkable advancement of smart grid technology within the IoT sector has raised substantial concerns over the privacy and security of the data collected and transferred in real-time. Therefore, ensuring security for smart meters and privacy for users is an urgent challenge. Secure data aggregation in smart metering systems remains a challenging task due to a plethora of attainable cyber and physical attacks. Many existing solutions suffer from computational complexity, time consumption, and security vulnerabilities. To address these challenges, this research proposes multiple privacy-preserving data aggregation protocols, combining watermarking and encryption, tailored for both low-frequency and high-frequency smart meters in resource-constrained environments. By leveraging the combined strengths of reversible watermarking and cryptographic techniques, these protocols ensure data confidentiality, integrity, and authenticity without reliance on any trusted third party. Experimental results for real IoT hardware are reported to confirm the computational efficiency of the proposed protocols, showcasing minimal energy and time consumption while maintaining robust resistance to various active and passive attacks. Additionally, comparative analyses reveal the superiority of the performance of the proposed protocols over existing schemes in the literature in terms of cost-effectiveness and scalability. Future research directions include exploring more lightweight cryptographic methods, developing more robust watermarking techniques--including the increase in the number of embedded bits at each time frame—considering multidimensional datasets, and refining comparative methodologies with a real-time scenario analysis. These advancements aim to enhance the applicability of the protocols in evolving smart grid environments, building upon the robust and practical solution for secure and efficient data aggregation in resource-limited smart metering systems presented in this research.