An Enhanced Privacy Protection Distributed State Estimation in Wireless Sensor Networks Against Eavesdropping Attacks

This paper investigates secure distributed state estimation in wireless sensor networks (WSNs), where the transmission of sensitive observed states among decentralized sensor nodes risks being intercepted by eavesdroppers, potentially exposing critical system information. To address this vulnerability, an enhanced privacy protection scheme is designed to safeguard real-state data. Specifically, the measurement state of each sensor is decomposed into public and private substates by the method of state decomposition, limiting information sharing to public substates among neighboring nodes. Furthermore, to counteract crafty eavesdroppers capable of reconstructing sensitive states through observer-based attacks, public substates are further transformed linearly and irreversibly, ensuring private substates remain concealed. The proposed enhanced privacy scheme is rigorously analyzed under scenarios involving measurement noise and two distinct eavesdropper models with varying capabilities, examining system convergence and privacy guarantees. Meanwhile, the privacy measurement and privacy-accuracy analysis are provided. Comprehensive simulation studies validate the efficacy of the proposed scheme.