Distributed Secure Filtering Against Eavesdropping Attacks in SINR-Based Sensor Networks

This paper focuses on the design of a privacy-preserving distributed Kalman filtering algorithm for a class of linear time-varying systems in signal-to-interference-plus-noise ratio (SINR)-based sensor networks, where packet dropouts may occur in information transmission between neighboring sensor nodes. Considering the potential occurrence of eavesdropping attacks during information transmission, which is common due to the inherent vulnerability of SINR-based sensor networks, a new class of distributed secure Kalman filtering algorithm is developed. The presented algorithm incorporates a modified ElGamal cryptosystem and adaptive fusion weights to significantly enhance security, resist privacy leakage, and bolster robustness against packet dropping. Then, a detailed performance analysis for the presented distributed secure Kalman filtering algorithm is conducted, where the security and unbiasedness of the designed algorithm are discussed. Sufficient conditions for the stability of the estimation error are further established to ensure that the estimation error is ultimately bounded in the almost sure sense. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithm.