5 citations · 6 across the 11 of their papers we have counts for
12 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…
PureSuperQMA(exp) = BellPureSymQMA(poly) = QMA via Dimension-Free Bosonic Argmax
William Gay, Fernando Granha Jeronimo, Lenny Liu +3
Pure-state consistency problems naturally lead to quantum proof systems in which a single pure witness must satisfy many acceptance constraints. The corresponding class $\mathsf{Pu…
The Honeycomb Framework for Code Bounds
William Gay, Fernando Granha Jeronimo, Lenny Liu
We introduce the honeycomb hierarchy, a representation-theoretic framework that gives new asymptotic upper bounds on . Its first level is the two-row hyperoctahedral repres…
Dimension-Free Polylogarithmic Quantum Shadow Tomography
Fernando Granha Jeronimo, Qizhao Huang, Lenny Liu
Shadow Tomography is a fundamental problem in quantum information theory. Given multiple copies of an unknown -dimensional quantum state and a known collection of observable…
Fairness risk and its privacy-enabled solution in AI-driven robotic applications
Le Liu, Bangguo Yu, Nynke Vellinga +1
Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions.…
Optimal Proximity Gap for Folded Reed--Solomon Codes via Subspace Designs
Fernando Granha Jeronimo, Lenny Liu, Pranav Rajpal
A collection of sets satisfies a -proximity gap with respect to some property if for every set in the collection, either (i) all members of the set are -close t…
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…