欺骗攻击下具备隐私保护的多智能体系统均值趋同控制

应晨铎; 伍益明; 徐明; 郑宁; 何熊熊

doi:10.16383/j.aas.c210889

欺骗攻击下具备隐私保护的多智能体系统均值趋同控制

doi: 10.16383/j.aas.c210889

应晨铎^1,,
伍益明^1,,
徐明^1,,
郑宁^1,,
何熊熊^2,

1.
杭州电子科技大学网络空间安全学院杭州 310018
2.
浙江工业大学信息工程学院杭州 310023

基金项目: 国家自然科学基金(61803135, 61873239, 62073109), 浙江省公益技术应用研究项目(LGF21F020011)资助

详细信息

作者简介:
应晨铎：杭州电子科技大学网络空间安全学院硕士研究生. 2020年获得浙大宁波理工学院软件工程学士学位. 主要研究方向为弹性趋同, 隐私保护和分布式系统安全. E-mail: cdying@hdu.edu.cn

伍益明：杭州电子科技大学网络空间安全学院副教授. 2016年获得浙江工业大学控制科学与工程博士学位. 主要研究方向为分布式系统安全控制, 多智能体系统网络安全和迭代学习控制. 本文通信作者. E-mail: ymwu@hdu.edu.cn

徐明：杭州电子科技大学网络空间安全学院教授. 2004年获得浙江大学博士学位. 主要研究方向为网络信息安全, 数字取证. E-mail: mxu@hdu.edu.cn

郑宁：杭州电子科技大学网络空间安全学院研究员. 1987年获得浙江大学硕士学位. 主要研究方向为信息安全, 信息管理系统和多智能体系统. E-mail: nzheng@hdu.edu.cn

何熊熊：浙江工业大学信息工程学院教授. 1997年获得浙江大学博士学位. 主要研究方向为迭代学习控制, 智能控制及其在多智能体系统和传感器网络中的应用. E-mail: hxx@zjut.edu.cn

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出版历程
- 收稿日期: 2021-09-15
- 录用日期: 2022-04-28
- 网络出版日期: 2022-05-19
- 刊出日期: 2023-02-20

Privacy-preserving Average Consensus Control forMulti-agent Systems Under Deception Attacks

1.
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018
2.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023

Funds: Supported by National Natural Science Foundation of China (61803135, 61873239, 62073109) and Zhejiang Provincial Public Welfare Research Project of China (LGF21F020011)

More Information

Author Bio:
YING Chen-Duo　Master student at the School of Cyberspace, Hangzhou Dianzi University. He received his bachelor degree in software engineering from NingboTech University in 2020. His research interest covers resilient consensus, privacy preservation, and distributed system security

WU Yi-Ming　Associate professor at the School of Cyberspace, Hangzhou Dianzi University. He received his Ph.D. degree in control science and engineering from Zhejiang University of Technology in 2016. His research interest covers distributed system secure control, cyber-security for multi-agent systems, and iterative learning control. Corresponding author of this paper

XU Ming　Professor at the School of Cyberspace, Hangzhou Dianzi University. He received his Ph.D. degree from Zhejiang University in 2004. His research interest covers network security and digital forensics

ZHENG Ning　Professor at the Sc-hool of Cyberspace, Hangzhou Dianzi University. He received his master degree from Zhejiang University in 1987. His research interest covers information security, information management system, and multi-agent systems

HE Xiong-Xiong Professor at the College of Information Engineering, Zhejiang University of Technology. He received his Ph.D. degree from Zhejiang University in 1997. His research inter-est covers iterative learning control, intelligent control and its applications in multi-agent systems and sensor networks

摘要

摘要: 针对通信网络遭受欺骗攻击的离散时间多智能体系统, 研究其均值趋同和隐私保护问题. 首先, 考虑链路信道存在窃听者的情形, 提出一种基于状态分解思想的分布式网络节点值重构方法, 以阻止系统初始信息的泄露. 其次, 针对所构建的欺骗攻击模型, 利用重构后节点状态信息并结合现有的安全接受广播算法, 提出一种适用于无向通信网络的多智能体系统均值趋同控制方法. 理论分析表明, 该方法能够有效保护节点初始状态信息的隐私, 并能消除链路中欺骗攻击的影响, 实现分布式系统中所有节点以初始值均值趋同. 最后, 通过数值仿真实验验证了该方法的有效性.
- 多智能体系统 /
- 均值趋同 /
- 欺骗攻击 /
- 隐私保护 /
- 网络安全
Abstract: This paper investigates the average consensus and privacy-preserving problem for discrete-time multi-agent systems with deception attacks. First, considering the situation where there are eavesdroppers on the link channel, a distributed node value reconstruction method based on the idea of state decomposition is proposed to prevent the leakage of the initial information of the system. Then, under the constructed deception attack model, a novel average consensus control method for multi-agent systems with undirected communication networks is proposed, which uses the reconstructed node status information and combines with the secure acceptance and broadcast algorithm. Theoretical analysis shows that the proposed method can effectively protect the privacy of the initial state information of the nodes, and eliminate the influence of deception attacks in the links, and reach a consensus. Finally, numerical simulation experiments verify the effectiveness of the proposed method.
- Multi-agent systems /
- average consensus /
- deception attack /
- privacy-preserving /
- cyber security

HTML全文

图 1 状态分解方法示例图

Fig. 1 Example diagram of state decomposition method

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图 2 欺骗攻击下多智能体系统分布式网络示意图

Fig. 2 The diagram of the multi-agent system distributed network under deception attacks

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图 3 6个节点组成的多智能体系统通信拓扑图

Fig. 3 Network topology of multi-agent system with 6 nodes

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图 4 系统不满足强(2f + 1)-链路鲁棒图下各节点的状态量测值变化轨迹

Fig. 4 State trajectory of each node with system that does not meet the strong (2f + 1)-links robustness

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图 5 系统外部通信链路遭受欺骗攻击下各节点的状态量测值变化轨迹

Fig. 5 State trajectory of each node under deception attack on the external communication link of the system

下载: 全尺寸图片幻灯片

图 6 节点$ v_4 $内部遭受欺骗攻击的通信拓扑及攻击示意图

Fig. 6 Communication topology and attack diagram of the deception attack inside node $ v_4 $

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图 7 系统内部通信链路遭受欺骗攻击下使用状态分解算法各节点的状态量测值变化轨迹

Fig. 7 State trajectory of each node under deceptionattack on the internal communication link of thesystem by using the statedecomposition algorithm

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图 8 系统内部通信链路遭受欺骗攻击下使用本文算法各节点的状态量测值变化轨迹

Fig. 8 State trajectory of each node under deception attack on the internal communication link of the system by using the proposed algorithm

下载: 全尺寸图片幻灯片

参考文献(34)

[1]	丁俐夫, 颜钢锋. 多智能体系统安全性问题及防御机制综述. 智能系统学报, 2020, 15(3): 425-434 Ding Li-Fu, Yan Gang-Feng. A survey of the security issues and defense mechanisms of multi-agent systems. CAAI Transactions on Intelligent Systems, 2020, 15(3): 425-434
[2]	李韬, 孟杨, 张纪锋. 多自主体量化趋同与有限数据率趋同综述. 自动化学报, 2013, 39(11): 1805-1811 doi: 10.3724/SP.J.1004.2013.01805 Li Tao, Meng Yang, Zhang Ji-Feng. An overview on quantized consensus and consensus with limited data rate of multi-agent systems. Acta Automatica Sinica, 2013, 39(11): 1805-1811 doi: 10.3724/SP.J.1004.2013.01805
[3]	王祥科, 李迅, 郑志强. 多智能体系统编队控制相关问题研究综述. 控制与决策, 2013, 28(11): 1601-1613 Wang Xiang-Ke, Li Xun, Zheng Zhi-Qiang. Survey of developments on multi-agent formation control related problems. Control and Decision, 2013, 28(11): 1601-1613
[4]	孙秋野, 滕菲, 张化光. 能源互联网及其关键控制问题. 自动化学报, 2017, 43(2): 176-194 Sun Qiu-Ye, Teng Fei, Zhang Hua-Guang. Energy internet and its key control issues. Acta Automatica Sinica, 2017, 43(2): 176-194
[5]	Huang Z, Mitra S, Dullerud G. Differentially private iterative synchronous consensus. In: Proceedings of the ACM Workshop on Privacy in the Electronic Society. New York, USA: 2012. 81−90
[6]	Huang Z, Mitra S, Vaidya N. Differentially private distributed optimization. In: Proceedings of the International Conference on Distributed Computing and Networking. New York, USA: 2015. 1−10
[7]	Nozari E, Tallapragada P, Cortes J. Differentially private average consensus: obstructions, trade-offs, and optimal algorithm design. Automatica, 2017, 81(7): 221-231
[8]	Katewa V, Pasqualetti F, Gupta V. On privacy vs cooperation in multi-agent systems. International Journal of Control, 2018, 91(7): 1693-1707 doi: 10.1080/00207179.2017.1326632
[9]	Manitara N, Hadjicostis C. Privacy-preserving asymptotic average consensus. In: Proceedings of the European Control Conference. Zurich, Switzerland: IEEE, 2013. 760−765
[10]	Kia S, Cortes J, Martinez S. Dynamic average consensus under limited control authority and privacy requirements. International Journal of Robust and Nonlinear Control, 2015, 25(13): 1941-1966 doi: 10.1002/rnc.3178
[11]	Pequito S, Kar S, Sundaram S, Aguiar A P. Design of communication networks for distributed computation with privacy guarantees. In: Proceedings of the 54th IEEE Conference on Decision and Control. Osaka, Japan: IEEE, 2015. 1370−1376
[12]	Alaeddini A, Morgansen K, Mesbahi M. Adaptive communication networks with privacy guarantees. In: Proceedings of the American Control Conference. Seattle, USA: IEEE, 2017. 4460− 4465
[13]	Hendriks R C, Erkin Z, Gerkmann T. Privacy preserving distributed beamforming based on homomorphic encryption. In: Proceedings of the 21st European Signal Processing Conference. Marrakech, Morocco: IEEE, 2013. 1−5
[14]	Hendriks R C, Erkin Z, Gerkmann T. Privacy-preserving distributed speech enhancement forwireless sensor networks by processing in the encrypted domain. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, Canada: IEEE, 2013. 7005−7009
[15]	Li Q, Cascudo I, Christensen M G. Privacy-preserving distributed average consensus based on additive secret sharing. In: Proceedings of the 27th European Signal Processing Conference. Coruna, Spain: IEEE, 2019. 1−5
[16]	Li Q, Christensen M G. A privacy-preserving asynchronous averaging algorithm based on shamir＇s secret sharing. In: Proceedings of the 27th European Signal Processing Conference. Coruna, Spain: IEEE, 2019. 1−5
[17]	Wang Y. Privacy-preserving average consensus via state decomposition. IEEE Transactions on Automatic Control, 2019, 64(11): 4711-4716 doi: 10.1109/TAC.2019.2902731
[18]	Zhang D, Liu L, Feng G. Consensus of heterogeneous linear multiagent systems subject to aperiodic sampled-data and dos attack. IEEE Transactions on Cybernetics, 2019, 49(4): 1501-1511 doi: 10.1109/TCYB.2018.2806387
[19]	Feng Z, Hu G. Secure cooperative event-triggered control of linear multiagent systems under dos attacks. IEEE Transactions on Control Systems Technology, 2020, 28(3): 741-752 doi: 10.1109/TCST.2019.2892032
[20]	Yang Y, Xu H, Yue D. Observer-based distributed secure consensus control of a class of linear multi-agent systems subject to random attacks. IEEE Transactions on Circuits and Systems I: Regular Papers, 2019, 66(8): 3089-3099 doi: 10.1109/TCSI.2019.2904747
[21]	Xu W, Hu G, Ho D W C, Feng Z. Distributed secure cooperative control under denial-of-service attacks from multiple adversaries. IEEE Transactions on Cybernetics, 2020, 50(8): 3458-3467 doi: 10.1109/TCYB.2019.2896160
[22]	Zhu M, Martínez S. On distributed constrained formation control in operator–vehicle adversarial networks. Automatica, 2013, 49(12): 3571-3582 doi: 10.1016/j.automatica.2013.09.031
[23]	Ding D, Wang Z, Ho D W C, Wei G. Observer-based event-triggering consensus control for multiagent systems with lossy sensors and cyber-attacks. IEEE Transactions on Cybernetics, 2016, 47(8): 1936-1947
[24]	He W, Gao X, Zhong W, Qian F. Secure impulsive synchronization control of multi-agent systems under deception attacks. Information Sciences, 2018, 459: 354-368 doi: 10.1016/j.ins.2018.04.020
[25]	Fu W, Qin J, Shi Y, Zheng W X, Kang Y. Resilient consensus of discrete-time complex cyber-physical networks under deception attacks. IEEE Transactions on Industrial Informatics, 2019, 16(7): 4868-4877
[26]	He W, Mo Z, Han Q L, Qian F. Secure impulsive synchronization in Lipschitz-type multi-agent systems subject to deception attacks. IEEE/CAA Journal of Automatica Sinica, 2020, 7(5): 1326-1334
[27]	Li H, Liao X, Huang T, Zhu W, Liu Y. Second-order global consensus in multiagent networks with random directional link failure. IEEE Transactions on Neural Networks and Learning Systems, 2014, 26(3): 565-575
[28]	Li H, Chen G, Huang T, Dong Z. High-performance consensus control in networked systems with limited bandwidth communication and time-varying directed topologies. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(5): 1043-1054
[29]	Lu Q, Liao X, Xiang T, Li H, Huang T. Privacy masking stochastic subgradient-push algorithm for distributed online optimization. IEEE Transactions on Cybernetics, 2020, 51(6): 3224-3237
[30]	Fiore D, Russo G. Resilient consensus for multi-agent systems subject to differential privacy requirements. Automatica, 2019, 106: 18-26 doi: 10.1016/j.automatica.2019.04.029
[31]	Zhang H, Sundaram S. Robustness of information diffusion algorithms to locally bounded adversaries. In: Proceedings of the American Control Conference. Montréal, Canada: IEEE, 2012. 5855−5861
[32]	Dibaji S M, Safi M, Ishii H. Resilient distributed averaging. In: Proceedings of the American Control Conference. Philadelphia, USA: 2019. 96−101
[33]	Hale M T, Egerstedty M. Differentially private cloud-based multi-agent optimization with constraints. In: Proceedings of the American Control Conference. Chicago, USA: 2015. 1235−1240
[34]	LeBlanc H J, Zhang H, Koutsoukos X, Sundaram S. Resilient asymptotic consensus in robust networks. IEEE Journal on Selected Areas in Communications, 2013, 31(4): 766-781 doi: 10.1109/JSAC.2013.130413