Secure and Privacy-Preserving Distributed Kalman Filtering for Sensor Networks With Event-Triggered Mechanism

Distributed Kalman filtering is a widely used technique for state estimation in sensor networks system. Typically, each node utilizes the measurement residuals and the information exchanged with the neighbors to obtain the fused state estimation. However, local measurement is susceptible to outliers, and data sharing leads to privacy and security issues. In this paper, a secure and privacy-preserving distributed Kalman filter (SPP-DKF) is developed to simultaneously address the aforementioned issues. Specifically, the outlier-resistant mechanism is embedded to improve the resilience of the distributed Kalman filter, which employs the saturation function to suppress the mutation of innovation caused by outliers. An event-triggered privacy-preserving scheme based on dynamic mask is designed to protect privacy of local state against different adversaries. Meanwhile, to ensure security, the digital signature based on homomorphic encryption and hash function is adopted to detect false data injection attacks. Furthermore, the mean-square estimation performance and the upper bound of the error covariance of the proposed SPP-DKF are analyzed. In addition, the privacy and security performance of the proposed algorithm are also ensured. Finally, simulation results indicate that the proposed SPP-DKF provides reliable estimation performance while ensuring the privacy and security of the exchanged data.