Distributed Confidentiality Fusion Estimation Against Eavesdroppers

This article is concerned with the distributed confidentiality fusion estimation problem for cyber-physical systems in the presence of eavesdroppers. A novel active contamination strategy is proposed to guarantee the confidentiality of local state estimates (LSEs) that are transmitted to the fusion center (FC) over communication channels. Here, the LSEs are actively contaminated by the contaminating vectors, which are related to the weighting fusion process. Meanwhile, the selecting matrices that denote whether the components are contaminated are, respectively, designed for linear and nonlinear systems by maximizing the mean square errors of eavesdropper’s estimator. Under this contamination strategy, the confidentiality of systems can be effectively guaranteed when the eavesdropper tries to obtain the real state by fusing the contaminated estimates, because the estimation error covariance of the eavesdropper is large. At the same time, the corresponding compensation strategy is employed in the FC to compensate the performance loss caused by the proposed contamination method. Finally, two illustrative examples are exploited to demonstrate the effectiveness of the proposed methods.