文献类型：期刊文献

英文题名：Privacy Preservation Against Enhanced Estimation by Kalman Smoother in Cloud-Based LQG Control Systems

作者：Zhou, Xia[1]; Lin, Wenhao[1]; Yang, Chao[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Department of Automation, Shanghai, China

年份：2024

起止页码：842

外文期刊名：14th Asian Control Conference, ASCC 2024

收录：EI(收录号：20244117170362)

语种：英文

外文关键词：Cloud platforms

摘要：This paper studies a privacy preservation problem in a cloud-based LQG control system. The system consists of a client and a cloud, where the client owns the plant to control and demands the optimal LQG control inputs for its plant, and thus it turns to the cloud for computation. The cloud computes the optimal LQG control inputs based on the shared states from the client. In this paper, a privacy filter is employed for the client to protect its states, which is seen by the client as privacy. Moreover, this paper considers a novel model that the cloud uses an additional Kalman smoother to further improve its knowledge which could cause more privacy leakage. This paper studies the design of the privacy filter against the cloud's enhanced estimation by a Kalman smoother, which is proposed as an optimization problem. Finally, this problem is solved efficiently by the proposed algorithm. ? 2024 Asian Control Association.