Privacy-Preserving Data Aggregation Techniques for Enhanced Efficiency and Security in Wireless Sensor Networks: A Comprehensive Analysis and Evaluation

TL;DR

The paper tackles privacy-preserving data aggregation in wireless sensor networks to reduce energy consumption and protect data from privacy threats in hostile environments. It proposes an in-network aggregation framework leveraging pattern-code based privacy (ESPADA) and robust statistics to achieve constant communication overhead $O(1)$ per cluster and resilience against active and passive attacks, including rogue base stations and kidnapped nodes. The study analyzes multiple aggregation architectures (centralised, tree-based, in-network, cluster-based) and demonstrates energy savings and reduced transmissions, while discussing trade-offs in data accuracy and scalability. The findings have practical impact for large-scale WSN deployments in domains such as environmental monitoring and infrastructure sensing.

Abstract

In this paper, we present a multidimensional, highly effective method for aggregating data for wireless sensor networks while maintaining privacy. The suggested system is resistant to data loss and secure against both active and passive privacy compromising attacks, such as the coalition attack from a rogue base station and kidnapped sensor nodes. With regard to cluster size, it achieves consistent communication overhead, which is helpful in large-scale WSNs. Due to its constant size communication overhead, the suggested strategy outperforms the previous privacy-preserving data aggregation scheme not only in terms of privacy preservation but also in terms of communication complexity and energy costs.