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To address the data security issue of distributed state estimation, this paper proposes a novel consensus-based cubature information filtering algorithm for sensitive nonlinear target tracking under restricted communication. Through a privacy-preserving approach via state decomposition, the algorithm can protect the privacy of local information from adversaries without sacrificing global estimation accuracy. Based on the push-sum consensus, the distributed approach is further extended to switching directed topologies, which is more feasible for tracking with communication constraints. Our method's average convergence, privacy preservation, and stability are theoretically proved. Finally, simulations are conducted to evaluate the algorithm's performance on security, accuracy, and robustness. • A distributed filter is developed for secure tracking with communication constraints. • The algorithm preserves each node's privacy without sacrificing estimation accuracy. • The algorithm's average convergence, privacy preservation, and stability are proved. [ABSTRACT FROM AUTHOR]