5 citations · 6 across the 6 of their papers we have counts for
7 papers
Design of Privacy-Preserving Estimators Under Prior Information Exposure
Le Liu, Yu Kawano, Ming Cao
State estimation is widely used in networked dynamical systems, but released estimates may inadvertently reveal sensitive information about private state variables. Existing privac…
A Randomized Scheduling Framework for Privacy-Preserving Multi-robot Rendezvous given Prior Information
Le Liu, Yu Kawano, Ming Cao
Privacy has become a critical concern in modern multi-robot systems, driven by both ethical considerations and operational constraints. As a result, growing attention has been dire…
Privacy protection under the exposure of systems' prior information
Le Liu, Yu Kawano, Ming Cao
For systems whose states implicate sensitive information, their privacy is of great concern. While notions like differential privacy have been successfully introduced to dynamical…
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
Le Liu, Yu Kawano, Antai Xie +1
In this paper, we investigate initial state privacy protection for discrete-time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy cha…
Distributionally Robust Model Order Reduction for Linear Systems
Le Liu, Yu Kawano, Yangming Dou +1
In this paper, we investigate distributionally robust model order reduction for linear, discrete-time, time-invariant systems. The external input is assumed to follow an uncertain…
Privacy Preservation for Statistical Input in Dynamical Systems
Le Liu, Yu Kawano, Ming Cao
This paper addresses the challenge of privacy preservation for statistical inputs in dynamical systems. Motivated by an autonomous building application, we formulate a privacy pres…
Design of Stochastic Quantizers for Privacy Preservation
Le Liu, Yu Kawano, Ming Cao
In this paper, we examine the role of stochastic quantizers for privacy preservation. We first employ a static stochastic quantizer and investigate its corresponding privacy-preser…