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State Estimation with Protecting Exogenous Inputs via Cramér-Rao Lower Bound Approach

组织者

康家熠

演讲者

郭利苹

时间

2025年03月27日 14:30 至 16:00

地点

A3-2-303

线上

Zoom 435 529 7909 (BIMSA)

摘要

This report addresses the real-time state estimation problem for dynamic systems while protecting exogenous inputs against adversaries, who may be honest-but-curious third parties or external eavesdroppers. The Cramér-Rao lower bound (CRLB) is employed to constrain the mean square error (MSE) of the adversary's estimate for the exogenous inputs above a specified threshold. By minimizing the MSE of the state estimate while ensuring a certain privacy level measured by CRLB, the problem is formulated as a constrained optimization. To solve the optimization problem, an explicit expression for CRLB is first provided. As the computational complexity of the CRLB increases with the time step, a low-complexity approach is proposed to make the complexity independent of time. Then, a relaxation approach is proposed to efficiently solve the optimization problem. Finally, a privacy-preserving state estimation algorithm with low complexity is developed, which also ensures (ε, δ)-differential privacy. Two illustrative examples, including a practical scenario for protecting building occupancy, demonstrate the effectiveness of the proposed algorithm.

演讲者介绍

Liping Guo received the B.S. and Ph.D. degrees in statistics and applied statistics from Sichuan University, Chengdu, China, in 2019 and 2023, respectively. She is currently a Postdoctoral Research Associate with the Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. Her current research interests include state estimation, privacy preservation, target tracking, and optimization algorithms.