Secure Estimation With Privacy Protection
针对用户敏感行为需保密的系统，提出一种隐私保护机制，并设计最优和次优估计器，通过优化实现隐私与性能的权衡。
In this article, we focus on the state estimation problems for a system with protecting user privacy. Regarding whether the user has conducted a sensitive action in the system as a kind of privacy, we propose a privacy-preserving mechanism (PPM) to prevent its action results from being disclosed or inferred. For such a system with the PPM, we first obtain the optimal estimator (OE). Subject to the inoperability of the OE in practice, we turn to designing a computationally efficient suboptimal estimator (SE) as an alternative. Then, we prove that this SE can remain stable while satisfying the user's requirements on both privacy protection and estimation performance. By solving a privacy-preserving optimization problem, a set of guidelines is established to customize a tradeoff between privacy and performance according to the user's demand. Finally, illustrated examples are used to illustrate the main theoretical results.