arXiv subjects

Yuqing Ni

Publications and source records attributed to Yuqing Ni.

8 recordsLinked to original sources

arXiv2024

Privacy Preservation by Intermittent Transmission in Cooperative LQG Control Systems

In this paper, we study a cooperative linear quadratic Gaussian (LQG) control system with a single user and a server. In this system, the user runs a process and employs the server to meet the needs of computation. However, the user regards its state trajectories as privacy. Therefore, we propose a privacy scheme, in which the user sends data to the server intermittently. By this scheme, the server's received information of the user is reduced, and consequently the user's privacy is preserved. In this paper, we consider a periodic transmission scheme. We analyze the performance of privacy preservation and LQG control of different transmission periods. Under the given threshold of the control performance loss, a trade-off optimization problem is proposed. Finally, we give the solution to the optimization problem.

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arXiv2022

Privacy Preservation by Local Design in Cooperative Networked Control Systems

In this paper, we study the privacy preservation problem in a cooperative networked control system, which has closed-loop dynamics, working for the task of linear quadratic Guassian (LQG) control. The system consists of a user and a server: the user owns the plant to control, while the server provides computation capability, and the user employs the server to compute control inputs for it. To enable the server's computation, the user needs to provide the measurements of the plant states to the server, who then calculates estimates of the states, based on which the control inputs are computed. However, the user regards the states as privacy, and makes an interesting request: the user wants the server to have "incorrect" knowledge of the state estimates rather than the true values. Regarding that, we propose a novel design methodology for the privacy preservation, in which the privacy scheme is locally equipped at the user side not open to the server, which manages to create a deviation in the server's knowledge of the state estimates from the true values. However, this methodology also raises significant challenges: in a closed-loop dynamic system, when the server's seized knowledge is incorrect, the system's behavior becomes complex to analyze; even the stability of the system becomes questionable, as the incorrectness will accumulate through the closed loop as time evolves. In this paper, we succeed in showing that the performance loss in LQG control caused by the proposed privacy scheme is bounded by rigorous mathematical proofs, which convinces the availability of the proposed design methodology. We also propose an associated novel privacy metric and obtain the analytical result on evaluating the privacy performance. Finally, we study the performance trade-off between privacy and control, where the accordingly proposed optimization problems are solved by numerical methods efficiently.

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arXiv2021

Multi-Party Dynamic State Estimation that Preserves Data and Model Privacy

In this paper we focus on the dynamic state estimation which harnesses a vast amount of sensing data harvested by multiple parties and recognize that in many applications, to improve collaborations between parties, the estimation procedure must be designed with the awareness of protecting participants' data and model privacy, where the latter refers to the privacy of key parameters of observation models. We develop a state estimation paradigm for the scenario where multiple parties with data and model privacy concerns are involved. Multiple parties monitor a physical dynamic process by deploying their own sensor networks and update the state estimate according to the average state estimate of all the parties calculated by a cloud server and security module. The paradigm taps additively homomorphic encryption which enables the cloud server and security module to jointly fuse parties' data while preserving the data privacy. Meanwhile, all the parties collaboratively develop a stable (or optimal) fusion rule without divulging sensitive model information. For the proposed filtering paradigm, we analyze the stabilization and the optimality. First, to stabilize the multi-party state estimator while preserving observation model privacy, two stabilization design methods are proposed. For special scenarios, the parties directly design their estimator gains by the matrix norm relaxation. For general scenarios, after transforming the original design problem into a convex semi-definite programming problem, the parties collaboratively derive suitable estimator gains based on the ADMM. Second, an optimal collaborative gain design method with model privacy guarantees is provided, which results in the asymptotic MMSE state estimation. Finally, numerical examples are presented to illustrate our design and theoretical findings.

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arXiv2020

An Optimal Linear Attack Strategy on Remote State Estimation

This work considers the problem of designing an attack strategy on remote state estimation under the condition of strict stealthiness and $ε$-stealthiness of the attack. An attacker is assumed to be able to launch a linear attack to modify sensor data. A metric based on Kullback-Leibler divergence is adopted to quantify the stealthiness of the attack. We propose a generalized linear attack based on past attack signals and the latest innovation. We prove that the proposed approach can obtain an attack that can cause more estimation performance loss than linear attack strategies recently studied in the literature. The result thus provides a bound on the tradeoff between available information and attack performance, which is useful in the development of mitigation strategies. Finally, some numerical examples are given to evaluate the performance of the proposed strategy.

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arXiv2019

On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors

We investigate the impact of Byzantine attacks in distributed detection under binary hypothesis testing. It is assumed that a fraction of the transmitted sensor measurements are compromised by the injected data from a Byzantine attacker, whose purpose is to confuse the decision maker at the fusion center. From the perspective of a Byzantine attacker, under the injection energy constraint, an optimization problem is formulated to maximize the asymptotic missed detection error probability, which is based on the Kullback-Leibler divergence. The properties of the optimal attack strategy are analyzed by convex optimization and parametric optimization methods. Based on the derived theoretic results, a coordinate descent algorithm is proposed to search the optimal attack solution. Simulation examples are provided to illustrate the effectiveness of the obtained attack strategy.

cs.IT↗

arXiv2019

A Novel Warehouse Multi-Robot Automation System with Semi-Complete and Computationally Efficient Path Planning and Adaptive Genetic Task Allocation Algorithms

We consider the problem of warehouse multi-robot automation system in discrete-time and discrete-space configuration with focus on the task allocation and conflict-free path planning. We present a system design where a centralized server handles the task allocation and each robot performs local path planning distributively. A genetic-based task allocation algorithm is firstly presented, with modification to enable heuristic learning. A semi-complete potential field based local path planning algorithm is then proposed, named the recursive excitation/relaxation artificial potential field (RERAPF). A mathematical proof is also presented to show the semi-completeness of the RERAPF algorithm. The main contribution of this paper is the modification of conventional artificial potential field (APF) to be semi-complete while computationally efficient, resolving the traditional issue of incompleteness. Simulation results are also presented for performance evaluation of the proposed path planning algorithm and the overall system.

cs.MA↗

arXiv2019

Time Synchronization Attack and Countermeasure for Multi-System Scheduling in Remote Estimation

We consider time synchronization attack against multi-system scheduling in a remote state estimation scenario where a number of sensors monitor different linear dynamical processes and schedule their transmissions through a shared collision channel. We show that by randomly injecting relative time offsets on the sensors, the malicious attacker is able to make the expected estimation error covariance of the overall system diverge without any system knowledge. For the case that the attacker has full system information, we propose an efficient algorithm to calculate the optimal attack, which spoofs the least number of sensors and leads to unbounded average estimation error covariance. To mitigate the attack consequence, we further propose a countermeasure by constructing shift invariant transmission policies and characterize the lower and upper bounds for system estimation performance. Simulation examples are provided to illustrate the obtained results.

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arXiv2017

Game-Theoretic Pricing and Selection with Fading Channels

We consider pricing and selection with fading channels in a Stackelberg game framework. A channel server decides the channel prices and a client chooses which channel to use based on the remote estimation quality. We prove the existence of an optimal deterministic and Markovian policy for the client, and show that the optimal policies of both the server and the client have threshold structures when the time horizon is finite. Value iteration algorithm is applied to obtain the optimal solutions for both the server and client, and numerical simulations and examples are given to demonstrate the developed result.

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