Distributed credible evidence fusion with privacy-preserving

Considering data safety in more and more applied peer-to-peer networks, such as wireless sensor networks, has become the focus of information fusion, this paper proposes the problem of credible evidence fusion (CEF) in a distributed system with privacy-preserving, where agent's raw evidence is shared only with authenticated neighbors while access or inference by non-neighbors is prevented. This privacy-preserving in a distributed style brings out two challenges never met in centralized CEF. One is about credibility calculation consensus (CCC), where the local evidence difference measure matrix (EDMM) faces elements missing for evidence unavailability to non-neighbors. The other is about privacy-preserving fusion consensus (PFC), because fusion based on raw evidence may lead to a counter-intuitive result. In CCC, missing elements in EDMM are recovered via low-rank matrix completion, and credibility consistency is further guaranteed via diffusion gradient descent. In PFC, a new measure named privacy iterative compensation term (PICT) is constructed based on credibility and discounted evidence so that raw evidence is impossibly inferred. Furthermore, the linear average consensus based on PICT is presented and proved to be converging to the centralized CEF if credibility is accurately given. The computational complexity is proportional to the cube of the number of agents and the square of the number of elements in the power set of the framework of discrimination. Simulation results demonstrate that the proposed method achieves credibility errors below 0.1, leading to fusion results and classification accuracy approaching those of centralized CEF, with a 30% improvement compared to RANSAC-based and COF-based methods.