A Comprehensive Review of Secure Aggregation of Environmental Data via Error-Bounded Encoding: Security Models, Optimization Techniques, and Emerging Computing Applications

Abstract

The rapid expansion of environmental monitoring systems, driven by the widespread adoption of Internet of Things devices and wireless sensor networks, has resulted in the generation of large volumes of distributed data. Ensuring secure aggregation of this data is essential for maintaining confidentiality, integrity, and efficiency in applications such as climate monitoring, smart agriculture, disaster management, and smart cities. Due to the resource-constrained nature of sensor devices and communication limitations, lightweight and privacy-preserving aggregation techniques are required. Secure aggregation protocols enable the computation of aggregate functions, such as sum and average, without exposing individual data values. Recent advancements have introduced error-bounded encoding techniques that allow approximate data representation within controlled error limits, reducing communication overhead while preserving analytical accuracy. This review examines secure aggregation methods incorporating such encoding strategies, including cryptographic approaches, coding-theoretic methods, federated learning-based techniques, and hybrid frameworks integrating edge computing and blockchain. While homomorphic encryption and secret sharing provide strong privacy guarantees, they often incur high computational costs, whereas encoding-based methods improve efficiency with minimal accuracy loss. Key challenges include balancing accuracy and efficiency, ensuring robustness against adversarial threats, and enabling real-time processing, highlighting the need for scalable and intelligent aggregation solutions.