Optimal Privacy-Preserving Transmission Schedule Against Eavesdropping Attacks on Remote State Estimation

被引：3

作者：

Zou, Jiaying ^{[1
,2
]}

Liu, Hanxiao ^{[1
,2
]}

Liu, Chun ^{[3
]}

Ren, Xiaoqiang ^{[3
]}

Wang, Xiaofan ^{[3
]}

机构：

[1] Shanghai Univ, Sch Future Technol, Shanghai 200444, Peoples R China

[2] Shanghai Univ, Inst Artificial Intelligence, Shanghai 200444, Peoples R China

[3] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China

来源：

IEEE CONTROL SYSTEMS LETTERS | 2024年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Noise; Eavesdropping; Optimal scheduling; Estimation error; State estimation; Schedules; Job shop scheduling; Cyber-physical systems; remote state estimation; eavesdropping attacks; transmission schedule;

D O I：

10.1109/LCSYS.2024.3398200

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter is concerned with the preservation of privacy in remote state estimation of cyber-physical systems. A privacy-preserving transmission scheduling strategy against eavesdropping is proposed, incorporating three operational modes for sensors: silence, direct transmission, and noise-injected transmission. This strategy is designed to minimize the transmission cost and estimation error covariance for the remote estimator while maximizing the estimation error covariance for eavesdroppers. Threshold structures are demonstrated for optimal transmission schedules in different scenarios. Additionally, a novel correlation between the optimal transmission choice and the magnitude of injection noise is presented, particularly pertinent to scenarios involving direct transmission and transmission with injection noise. This correlation is important in balancing transmission information integrity against privacy concerns. Finally, several numerical examples are presented to demonstrate the effectiveness of the theoretical results.

引用

页码：538 / 543

页数：6

共 50 条

[21] Optimal Linear Encryption Against Stealthy Attacks on Remote State Estimation
Shang, Jun
Chen, Maoyin
Chen, Tongwen
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 66 (08) : 3592 - 3607
[22] Optimal Eavesdropping Problem in Privacy Preserving Consensus
Zhou, Han
Yang, Wen
Yang, Chao
Tang, Yang
Shi, Hongbo
IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2017, : 5929 - 5934
[23] Privacy-preserving co-synthesis against sensor-actuator eavesdropping intruder?
Tai, Ruochen
Lin, Liyong
Zhu, Yuting
Su, Rong
AUTOMATICA, 2023, 150
[24] A Verifiable Privacy-Preserving Federated Learning Framework Against Collusion Attacks
Chen, Yange
He, Suyu
Wang, Baocang
Feng, Zhanshen
Zhu, Guanghui
Tian, Zhihong
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2025, 24 (05) : 3918 - 3934
[25] On the effectiveness of graph matching attacks against privacy-preserving record linkage
Heng, Youzhe
Armknecht, Frederik
Chen, Yanling
Schnell, Rainer
PLOS ONE, 2022, 17 (09):
[26] Privacy-preserving generative framework for images against membership inference attacks
Yang, Ruikang
Ma, Jianfeng
Miao, Yinbin
Ma, Xindi
IET COMMUNICATIONS, 2023, 17 (01) : 45 - 62
[27] Efficient Privacy-Preserving Federated Learning Against Inference Attacks for IoT
Miao, Yifeng
Chen, Siguang
2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC, 2023,
[28] Privacy-Preserving Network Embedding Against Private Link Inference Attacks
Han, Xiao
Yang, Yuncong
Wang, Leye
Wu, Junjie
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2024, 21 (02) : 847 - 859
[29] Optimal Transmission Strategy for Sensors to Defend against Eavesdropping and Jamming Attacks
Yuan, Li
Wang, Kun
Miyazaki, Toshiaki
Guo, Song
Wu, Meng
2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,
[30] DefendFL: A Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks
Liu, Jiao
Li, Xinghua
Liu, Ximeng
Zhang, Haiyan
Miao, Yinbin
Deng, Robert H.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024,

← 1 2 3 4 5 →