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A survey on privacy-preserving control and filtering of networked control systems. (English) Zbl 07903851

Summary: With the increasing utilisation of information technology and artificial intelligence in practical control systems, particularly in large-scale distributed networked systems, growing concerns have arisen regarding the potential disclosure of individuals’ sensitive information to adversaries. Consequently, safeguarding privacy security against the rapidly increasing risk of privacy leakages has become a top priority in modern control systems. This survey aims to offer a comprehensive review of privacy-preserving control and filtering problems in networked control system. First, we review some basic introductions to the privacy-preserving mechanisms from the perspective of control community. Then, we present recent advancements in the design of privacy-preserving strategies for various control and filtering problems. Moreover, several possible future research topics are outlined on the privacy-preserving issue of control systems.

MSC:

93E11 Filtering in stochastic control theory
93B70 Networked control
93B07 Observability
94A60 Cryptography
93-02 Research exposition (monographs, survey articles) pertaining to systems and control theory
Full Text: DOI

References:

[1] Alanwar, A., Gassmann, V., He, X., Said, H., Sandberg, H., Johansson, K. H., & Althoff, M. (2023). Privacy-preserving set-based estimation using partially homomorphic encryption. European Journal of Control, 71. Article 100786. · Zbl 1516.94021
[2] Altafini, C. (2020). A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics. Automatica, 122. Article 109253. · Zbl 1451.93011
[3] Calis, M., Heusdens, R., & Hendriks, R. C. (2021). A privacy-preserving asynchronous averaging algorithm based on state decomposition. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 2115-2119). Institute of Electrical and Electronics Engineers Inc.
[4] Carvalho, T., Moniz, N., Faria, P., & Antunes, L. (2023). Survey on privacy-preserving techniques for microdata publication. ACM Computing Surveys, 55(14), 1-42.
[5] Chen, B., Leahy, K., Jones, A., & Hale, M. (2023). Differential privacy for symbolic systems with application to Markov Chains. Automatica, 152. Article 110908. · Zbl 07701199
[6] Cortés, J., Dullerud, G. E., Han, S., Le Ny, J., Mitra, S., & Pappas, G. J. (2016). Differential privacy in control and network systems. In 2016 IEEE 55th Conference on Decision and Control (CDC) (pp. 4252-4272). Institute of Electrical and Electronics Engineers Inc.
[7] Degue, K. H., & Le Ny, J. (2023a). Cooperative differentially private LQG control with measurement aggregation. IEEE Control Systems Letters, 7, 1093-1098.
[8] Degue, K. H., & Le Ny, J. (2023b). Differentially private Kalman filtering with signal aggregation. IEEE Transactions on Automatic Control, 68(10), 6240-6246. · Zbl 07794467
[9] Deng, Y., Léchappé, V., Moulay, E., Chen, Z., Liang, B., Plestan, F., & Han, Q.-L. (2022). Predictor-based control of time-delay systems: A survey. International Journal of Systems Science, 53(12), 2496-2534. · Zbl 1504.93211
[10] Ding, L., & Sun, W. (2023). Predefined time fuzzy adaptive control of switched fractional-order nonlinear systems with input saturation. International Journal of Network Dynamics and Intelligence, 2(4). Article 100019.
[11] Ding, W., Yang, W., Zhou, J., Shi, L., & Chen, G. (2022). Privacy preserving via secure summation in distributed Kalman filtering. IEEE Transactions on Control of Network Systems, 9(3), 1481-1492.
[12] Dong, S.-S., Li, Y.-G., & An, L. (2023). Optimal strictly stealthy attacks in cyber-physical systems with multiple channels under the energy constraint. International Journal of Systems Science, 54(13), 2608-2625. · Zbl 1530.93150
[13] Dong, T., Bu, X., & Hu, W. (2020). Distributed differentially private average consensus for multi-agent networks by additive functional Laplace noise. Journal of the Franklin Institute, 357(6), 3565-3584. · Zbl 1437.93119
[14] Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography (pp. 265-284). Springer Berlin Heidelberg. · Zbl 1112.94027
[15] Fang, W., Zamani, M., & Chen, Z. (2021). Secure and privacy preserving consensus for second-order systems based on paillier encryption. Systems & Control Letters, 148. Article 104869. · Zbl 1478.93623
[16] Farokhi, F., Shames, I., & Batterham, N. (2017). Secure and private control using semi-homomorphic encryption. Control Engineering Practice, 67, 13-20.
[17] Feng, Y., Li, X., Shi, D., & Dai, D. (2023). An efficient robust model predictive control for nonlinear Markov jump systems with persistent disturbances using matrix partition. International Journal of Systems Science, 54(10), 2118-2133. · Zbl 1520.93133
[18] Feng, Y., Wang, F., Duan, F., Liu, Z., & Chen, Z. (2022). Anonymous privacy-preserving consensus via mixed encryption communication. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(8), 3445-3449.
[19] Fereydounian, M., Mokhtari, A., Pedarsani, R., & Hassani, H. (2023). Provably private distributed averaging consensus: An information-theoretic approach. IEEE Transactions on Information Theory, 69(11), 7317-7335. · Zbl 07884066
[20] Ferrari, E., Tian, Y., Sun, C., Li, Z., & Wang, C. (2022). Privacy-preserving design of scalar LQG control. Entropy, 24, 7856.
[21] Fiore, D., & Russo, G. (2019). Resilient consensus for multi-agent systems subject to differential privacy requirements. Automatica, 106, 18-26. · Zbl 1429.93329
[22] Gao, C., He, X., Dong, H., Liu, H., & Lyu, G. (2022). A survey on fault-tolerant consensus control of multi-agent systems: Trends, methodologies and prospects. International Journal of Systems Science, 53(13), 2800-2813. · Zbl 1504.93348
[23] Gao, C., Wang, Z., He, X., & Dong, H. (2022). Fault-tolerant consensus control for multiagent systems: An encryption-decryption scheme. IEEE Transactions on Automatic Control, 67(5), 2560-2567. · Zbl 1537.93671
[24] Gao, C., Wang, Z., He, X., & Yue, D. (2021). Sampled-data-based fault-tolerant consensus control for multi-agent systems: A data privacy preserving scheme. Automatica, 133. Article 109847. · Zbl 1480.93250
[25] Gao, H., Li, Y., Yu, L., & Yu, H. (2023). Collaborative-prediction-based recursive filtering for nonlinear systems with sensor saturation under duty cycle scheduling. Systems Science & Control Engineering, 11(1). Article 2247007.
[26] Gao, H., Li, Z., & Wang, Y. (2022). Privacy-preserving collaborative estimation for networked vehicles with application to collaborative road profile estimation. IEEE Transactions on Intelligent Transportation Systems, 23(10), 17301-17311.
[27] Gao, L., Zhou, Y., Chen, X., Cai, R., Chen, G., & Li, C. (2023). Privacy-preserving dynamic average consensus via random number perturbation. IEEE Transactions on Circuits and Systems II: Express Briefs, 70(4), 1490-1494.
[28] Guo, X., Bi, Z., Wang, J., Qin, S., Liu, S., & Qi, L. (2023). Reinforcement learning for disassembly system optimization problems: A survey. International Journal of Network Dynamics and Intelligence, 2(1), 1-14.
[29] Gupta, N., Katz, J., & Chopra, N. (2017). Privacy in distributed average consensus. IFAC-PapersOnLine, 50(1), 9515-9520.
[30] Gusrialdi, A. (2023). Resilient and privacy-preserving leader-follower consensus in presence of cyber-attacks. IEEE Control Systems Letters, 7, 3211-3216.
[31] Han, F., Liu, J., Li, J., Song, J., Wang, M., & Zhang, Y. (2023). Consensus control for multi-rate multi-agent systems with fading measurements: the dynamic event-triggered case. Systems Science & Control Engineering, 11(1). Article 2158959.
[32] He, J., Cai, L., & Guan, X. (2018). Preserving data-privacy with added noises: Optimal estimation and privacy analysis. IEEE Transactions on Information Theory, 64(8), 5677-5690. · Zbl 1401.94159
[33] He, J., Cai, L., & Guan, X. (2020). Differential private noise adding mechanism and its application on consensus algorithm. IEEE Transactions on Signal Processing, 68, 4069-4082. · Zbl 07591020
[34] Hou, J., Wang, J., Zhang, M., Jin, Z., Wei, C., & Ding, Z. (2023). Privacy-preserving resilient consensus for multi-agent systems in a general topology structure. ACM Transactions on Privacy and Security, 26(3), 1-22.
[35] Hu, J., Sun, Q., Wang, R., Wang, B., Zhai, M., & Zhang, H. (2022). Privacy-preserving sliding mode control for voltage restoration of AC microgrids based on output mask approach. IEEE Transactions on Industrial Informatics, 18(10), 6818-6827.
[36] Hu, J., Sun, Q., Zhai, M., & Wang, B. (2023). Privacy-preserving consensus strategy for secondary control in microgrids against multilink false data injection attacks. IEEE Transactions on Industrial Informatics, 19(10), 10334-10343.
[37] Hu, Z., Hu, J., Tan, H., Huang, J., & Cao, Z. (2022). Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol. International Journal of Systems Science, 53(13), 2786-2799. · Zbl 1504.93376
[38] Huang, J., Gao, C., & He, X. (2022). Privacy-preserving state estimation with unreliable channels. ISA Transactions, 127, 4-12.
[39] Huang, Z., Mitra, S., & Dullerud, G. (2012). Differentially private iterative synchronous consensus. In Proceedings of the 2012 ACM Workshop on Privacy in the Electronic Society (pp. 81-90). Association for Computing Machinery.
[40] Huang, Z., Wang, Y., Mitra, S., & Dullerud, G. E. (2014). On the cost of differential privacy in distributed control systems. In Proceedings of the 3rd International Conference on High Confidence Networked Systems (pp. 105-114). Association for Computing Machinery.
[41] Huo, X., & Liu, M. (2022). Distributed privacy-preserving electric vehicle charging control based on secret sharing. Electric Power Systems Research, 211. Article 108357.
[42] Jia, M., Huang, S., Wang, Z., & Shen, C. (2021). Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error. Renewable Energy, 163, 1318-1332.
[43] Jia, R., Dong, R., Sastry, S. S., & Sapnos, C. J. (2017). Privacy-enhanced architecture for occupancy-based HVAC control. In 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS) (pp. 177-186). Institute of Electrical and Electronics Engineers Inc.
[44] Kawano, Y., & Cao, M. (2020). Design of privacy-preserving dynamic controllers. IEEE Transactions on Automatic Control, 65(9), 3863-3878. · Zbl 1533.93431
[45] Khedher, M. I., Jmila, H., & El Yacoubi, M. (2023). On the formal evaluation of the robustness of neural networks and its pivotal relevance for AI-based safety-critical domains. International Journal of Network Dynamics and Intelligence, 2(4). Article 100018.
[46] Kogiso, K., & Fujita, T. (2015). Cyber-security enhancement of networked control systems using homomorphic encryption. In 2015 54th IEEE Conference on Decision and Control (CDC) (pp. 6836-6843). Institute of Electrical and Electronics Engineers Inc.
[47] Le Ny, J. (2020). Differentially private nonlinear observer design using contraction analysis. International Journal of Robust and Nonlinear Control, 30(11), 4225-4243. · Zbl 1466.93070
[48] Le Ny, J., & Mohammady, M. (2018). Differentially private MIMO filtering for event streams. IEEE Transactions on Automatic Control, 63(1), 145-157. · Zbl 1390.93760
[49] Le Ny, J., & Pappas, G. J. (2014). Differentially private filtering. IEEE Transactions on Automatic Control, 59(2), 341-354. · Zbl 1360.93701
[50] Li, H., Chen, Y., Li, K., Wang, C., & Chen, B. (2023). Transportation internet: A sustainable solution for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 24(12), 15818-15829.
[51] Li, Q., Xue, H., Pan, Y., & Liang, H. (2023). Dynamic output feedback control for interval type-2 fuzzy systems against dos attacks and sensor failures. International Journal of Systems Science, 54(15), 2904-2920. · Zbl 1530.93124
[52] Li, T., Wang, H., He, D., & Yu, J. (2022). Blockchain-based privacy-preserving and rewarding private data sharing for IoT. IEEE Internet of Things Journal, 9(16), 15138-15149.
[53] Li, W., & Yang, F. (2023). Information fusion over network dynamics with unknown correlations: An overview. International Journal of Network Dynamics and Intelligence, 2(2). Article 100003.
[54] Li, X., Li, M., Yan, P., Li, G., Jiang, Y., Luo, H., & Yin, S. (2023). Deep learning attention mechanism in medical image analysis: Basics and beyonds. International Journal of Network Dynamics and Intelligence, 2(1), 93-116.
[55] Li, X., & Ye, D. (2023). Dynamic event-triggered distributed filtering design for interval type-2 fuzzy systems over sensor networks under deception attacks. International Journal of Systems Science, 54(15), 2875-2890. · Zbl 1530.93275
[56] Liang, C., Ge, M., Xu, J., Liu, Z., & Liu, F. (2022). Secure and privacy-preserving formation control for networked marine surface vehicles with sampled-data interactions. IEEE Transactions on Vehicular Technology, 71(2), 1307-1318.
[57] Liang, L., Ding, R., & Liu, S. (2023). Event-triggered privacy preserving consensus control with edge-based additive noise. arXiv:2303.10547.
[58] Liang, S., Lam, J., & Lin, H. (2023). Secure estimation with privacy protection. IEEE Transactions on Cybernetics, 53(8), 4947-4961.
[59] Liang, W., Fan, Y., Li, K.-C., Zhang, D., & Gaudiot, J.-L. (2020). Secure data storage and recovery in industrial blockchain network environments. IEEE Transactions on Industrial Informatics, 16(10), 6543-6552.
[60] Liang, W., Xiao, L., Zhang, K., Tang, M., He, D., & Li, K.-C. (2022). Data fusion approach for collaborative anomaly intrusion detection in blockchain-based systems. IEEE Internet of Things Journal, 9(16), 14741-14751.
[61] Liao, J., Lam, H.-K., Gulati, S., & Hayee, B. (2023). Improved computer-aided diagnosis system for nonerosive reflux disease using contrastive self-supervised learning with transfer learning. International Journal of Network Dynamics and Intelligence, 2(3). Article 100010.
[62] Liu, C.-C., Chen, C.-C., Chen, Y.-Y., & Liao, T.-L. (2023). Consensus control of discrete-time multi-agent systems with privacy preservation. Asian Journal of Control, 25(5), 3431-3442. · Zbl 07892390
[63] Liu, X., Wang, Y., Xiao, J., Chi, M., & Liu, Z. (2022). Concentrated differentially private average consensus algorithm for a discrete-time network with heterogeneous dynamics. Journal of the Franklin Institute, 359(4), 1655-1676. · Zbl 1483.93581
[64] Lu, Y., Dai, Y., & Zhang, Y. (2020). Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics, 16(6), 4177-4186.
[65] Lu, Y., & Zhu, M. (2019). A control-theoretic perspective on cyber-physical privacy: Where data privacy meets dynamic systems. Annual Reviews in Control, 47, 423-440.
[66] Maity, D., & Tsiotras, P. (2022). Multiagent consensus subject to communication and privacy constraints. IEEE Transactions on Control of Network Systems, 9(2), 943-955.
[67] Miao, Y., Liu, X., Choo, K.-K. R., Deng, R. H., Li, J., Li, H., & Ma, J. (2021). Privacy-preserving attribute-based keyword search in shared multi-owner setting. IEEE Transactions on Dependable and Secure Computing, 18(3), 1080-1094.
[68] Mo, Y., & Murray, R. M. (2017). Privacy preserving average consensus. IEEE Transactions on Automatic Control, 62(2), 753-765. · Zbl 1364.91048
[69] Moradi, A., Venkategowda, N. K. D., Talebi, S. P., & Werner, S. (2022). Privacy-preserving distributed Kalman filtering. IEEE Transactions on Signal Processing, 70, 3074-3089. · Zbl 07911458
[70] Nekouei, E., Sandberg, H., Skoglund, M., & Johansson, K. H. (2022). Optimal privacy-aware estimation. IEEE Transactions on Automatic Control, 67(5), 2253-2266. · Zbl 1537.93723
[71] Nekouei, E., Sandberg, H., Skoglund, M., & Johansson, K. H. (2023). A model randomization approach to statistical parameter privacy. IEEE Transactions on Automatic Control, 68(2), 839-850. · Zbl 1541.93363
[72] Nekouei, E., Tanaka, T., Skoglund, M., & Johansson, K. H. (2019). Information-theoretic approaches to privacy in estimation and control. Annual Reviews in Control, 47, 412-422.
[73] Ni, Y., Wu, J., Li, L., & Shi, L. (2021). Multi-party dynamic state estimation that preserves data and model privacy. IEEE Transactions on Information Forensics and Security, 16, 2288-2299.
[74] Nozari, E., Tallapragada, P., & Cortés, J. (2015). Differentially private average consensus with optimal noise selection. IFAC-PapersOnLine, 48(22), 203-208.
[75] Nozari, E., Tallapragada, P., & Cortés, J. (2017). Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design. Automatica, 81, 221-231. · Zbl 1372.93027
[76] Paillier, P. (1999). Public-key cryptosystems based on composite degree residuosity classes. In International Conference on the Theory and Applications of Cryptographic Techniques (pp. 223-238). Springer. · Zbl 0933.94027
[77] Pan, D., Ding, D., Ge, X., Han, Q.-L., & Zhang, X.-M. (2023). Privacy-preserving platooning control of vehicular cyber-physical systems with saturated inputs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2083-2097.
[78] Pang, Z.-H., Fan, L.-Z., Guo, H., Shi, Y., Chai, R., Sun, J., & Liu, G.-P. (2022). Security of networked control systems subject to deception attacks: A survey. International Journal of Systems Science, 53(16), 3577-3598. · Zbl 1518.93051
[79] Pang, Z.-H., Mu, T., Hou, F.-Y., Shi, Y., & Sun, J. (2023). Two networked predictive control methods for output tracking of networked systems with plant-model mismatch. International Journal of Systems Science, 54(10), 2073-2088. · Zbl 1520.93197
[80] Pokhrel, S. R., & Choi, J. (2020). Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. IEEE Transactions on Communications, 68(8), 4734-4746.
[81] Qian, X., & Cui, B. (2023). A mobile sensing approach to distributed consensus filtering of 2D stochastic nonlinear parabolic systems with disturbances. Systems Science & Control Engineering, 11(11). Article 2167885.
[82] Ramos, G., & Pequito, S. (2023). Designing communication networks for discrete-time consensus for performance and privacy guarantees. Systems & Control Letters, 180. Article 105608. · Zbl 1530.93480
[83] Rikos, A. I., Charalambous, T., Johansson, K. H., & Hadjicostis, C. N. (2023). Distributed event-triggered algorithms for finite-time privacy-preserving quantized average consensus. IEEE Transactions on Control of Network Systems, 10(1), 38-50.
[84] Ruan, M., Gao, H., & Wang, Y. (2019). Secure and privacy-preserving consensus. IEEE Transactions on Automatic Control, 64(10), 4035-4049. · Zbl 1482.91071
[85] Ryu, M., & Kim, K. (2022). A privacy-preserving distributed control of optimal power flow. IEEE Transactions on Power Systems, 37(3), 2042-2051.
[86] Schlüter, N., Binfet, P., & Darup, M. S. (2023). Cryptanalysis of random affine transformations for encrypted control. IFAC-PapersOnLine, 56(2), 11209-11216.
[87] Shen, X., & Liu, Y. (2022). Privacy-preserving distributed estimation over multitask networks. IEEE Transactions on Aerospace and Electronic Systems, 58(3), 1953-1965.
[88] Sultangazin, A., & Tabuada, P. (2021). Symmetries and isomorphisms for privacy in control over the cloud. IEEE Transactions on Automatic Control, 66(2), 538-549. · Zbl 07320179
[89] Suo, J., & Li, N. (2022). Observer-based synchronisation control for discrete-time delayed switched complex networks with coding-decoding approach. International Journal of Systems Science, 53(13), 2711-2728. · Zbl 1504.93362
[90] Taheri, M., Khorasani, K., Shames, I., & Meskin, N. (2021). Towards privacy preserving consensus control in multi-agent cyber-physical systems subject to cyber attacks. In 2021 European Control Conference (ECC) (pp. 939-945). Institute of Electrical and Electronics Engineers Inc.
[91] Tao, H., Tan, H., Chen, Q., Liu, H., & Hu, J. (2022). \( h_{\infty }\) state estimation for memristive neural networks with randomly occurring dos attacks. Systems Science & Control Engineering, 10(1), 154-165.
[92] Tran, H.-Y., Hu, J., & Pota, H. R. (2023). A privacy-preserving state estimation scheme for smart grids. IEEE Transactions on Dependable and Secure Computing, 20(5), 3940-3956.
[93] Wan, X., Guo, Y., & Wu, X. (2023). Differentially private consensus for multi-agent systems under switching topology. IEEE Transactions on Circuits and Systems II: Express Briefs, 70(9), 3499-3503.
[94] Wang, A., He, H., & Liao, X. (2021). Event-triggered privacy-preserving average consensus for multiagent networks with time delay: An output mask approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(7), 4520-4531.
[95] Wang, A., Liu, W., Li, T., & Huang, T. (2021). Privacy-preserving weighted average consensus and optimal attacking strategy for multi-agent networks. Journal of the Franklin Institute, 358(6), 3033-3050. · Zbl 1464.93073
[96] Wang, A., Liu, Y., & Huang, T. (2022). Event-triggered privacy-preserving average consensus for continuous-time multi-agent network systems. Journal of the Franklin Institute, 359(10), 4959-4975. · Zbl 1491.93114
[97] Wang, J., Tan, J., & Zhang, J.-F. (2023). Differentially private distributed parameter estimation. Journal of Systems Science and Complexity, 36(1), 187-204. · Zbl 1512.94102
[98] Wang, J., Zhang, J.-F., & Liu, X.-K. (2022). Differentially private resilient distributed cooperative online estimation over digraphs. International Journal of Robust and Nonlinear Control, 32(15), 8670-8688. · Zbl 1528.93080
[99] Wang, L., & Cao, X. (2019). On optimal privacy-preserving transmission schedule for remote state estimation in the presence of an eavesdropper. In 2019 1st International Conference on Industrial Artificial Intelligence (IAI) (pp. 1-5). Institute of Electrical and Electronics Engineers Inc.
[100] Wang, L., Manchester, I. R., Trumpf, J., & Shi, G. (2023). Differential initial-value privacy and observability of linear dynamical systems. Automatica, 148. Article 110722. · Zbl 1507.93042
[101] Wang, X., He, J., Cheng, P., & Chen, J. (2017). Differentially private maximum consensus. IFAC-PapersOnLine, 50(1), 9509-9514. 20th IFAC World Congress.
[102] Wang, X., He, J., Cheng, P., & Chen, J. (2019). Differentially private maximum consensus: Design, analysis and impossibility result. IEEE Transactions on Network Science and Engineering, 6(4), 928-939.
[103] Wang, Y. (2019). Privacy-preserving average consensus via state decomposition. IEEE Transactions on Automatic Control, 64(11), 4711-4716. · Zbl 1482.93029
[104] Wang, Y., Huang, Z., Mitra, S., & Dullerud, G. E. (2017). Differential privacy in linear distributed control systems: Entropy minimizing mechanisms and performance tradeoffs. IEEE Transactions on Control of Network Systems, 4(1), 118-130. · Zbl 1370.94619
[105] Wang, Y., Lam, J., & Lin, H. (2021). Differentially private average consensus with general directed graphs. Neurocomputing, 458, 87-98.
[106] Wang, Y., Lam, J., & Lin, H. (2022). Consensus of linear multivariable discrete-time multiagent systems: Differential privacy perspective. IEEE Transactions on Cybernetics, 52(12), 13915-13926.
[107] Wang, Y., Lam, J., & Lin, H. (2023). Differentially private average consensus for networks with positive agents. IEEE Transactions on Cybernetics, 1-14.
[108] Wang, Y., Liu, H., & Tan, H. (2023). An overview of filtering for sampled-data systems under communication constraints. International Journal of Network Dynamics and Intelligence, 2(3). Article 100011.
[109] Wang, Y., Lu, J., Zheng, W. X., & Shi, K. (2021). Privacy-preserving consensus for multi-agent systems via node decomposition strategy. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(8), 3474-3484.
[110] Wang, Y., Mitra, S., & Dullerud, G. E. (2017). Differential privacy and minimum-variance unbiased estimation in multi-agent control systems. 20th IFAC World Congress, 50(1), 9521-9526.
[111] Wang, Z., Ma, M., Zhou, Q., Xiong, L., Wang, L., Wang, J., & Wang, J. (2022). A privacy-preserving distributed control strategy in islanded AC microgrids. IEEE Transactions on Smart Grid, 13(5), 3369-3382.
[112] Xie, M., Bai, Y., Huang, M., & Hu, Z. (2017). Multiorder fusion data privacy-preserving scheme for wireless sensor networks. Journal of Electrical and Computer Engineering, Article 3956027.
[113] Xiong, Y., & Li, Z. (2022). Privacy-preserved average consensus algorithms with edge-based additive perturbations. Automatica, 140. Article 110223. · Zbl 1485.93542
[114] Xu, H., Ni, Y.-H., Liu, Z., & Chen, Z. (2020). Privacy-preserving leader-following consensus via node-augment mechanism. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(6), 2117-2121.
[115] Yan, J., Meng, Y., Yang, X., Luo, X., & Guan, X. (2021). Privacy-preserving localization for underwater sensor networks via deep reinforcement learning. IEEE Transactions on Information Forensics and Security, 16, 1880-1895.
[116] Yan, X., Chen, B., Zhang, Y., & Yu, L. (2023). Guaranteeing differential privacy in distributed fusion estimation. IEEE Transactions on Aerospace and Electronic Systems, 59(3), 3416-3423.
[117] Yang, F., Li, J., Dong, H., & Shen, Y. (2022). Proportional-integral-type estimator design for delayed recurrent neural networks under encoding-decoding mechanism. International Journal of Systems Science, 53(13), 2729-2741. · Zbl 07614008
[118] Yang, Y., Yang, H., Yu, M., & Sun, Y. (2021). Privacy-preserving consensus of continuous multi-agent systems with communication delay. In Proceedings of 2020 Chinese Intelligent Systems Conference: Volume II (pp. 249-256). Springer.
[119] Yang, Z., Liu, Y., Zhang, W., Alsaadi, F. E., & Alharbi, K. H. (2022). Differentially private containment control for multi-agent systems. International Journal of Systems Science, 53(13), 2814-2831. · Zbl 1504.93019
[120] Yao, A. C. (1982). Protocols for secure computations. In 23rd Annual Symposium on Foundations of Computer Science (SFCS 1982) (pp. 160-164). Institute of Electrical and Electronics Engineers Inc.
[121] Yao, F., Ding, Y., Hong, S., & Yang, S. (2022). A survey on evolved LoRa-based communication technologies for emerging internet of things applications. International Journal of Network Dynamics and Intelligence, 1(1), 4-19.
[122] Yazdani, K., Jones, A., Leahy, K., & Hale, M. (2023). Differentially private LQ control. IEEE Transactions on Automatic Control, 68(2), 1061-1068. · Zbl 1541.93020
[123] Yi, X., Yu, H., Fang, Z., & Ma, L. (2023). Probability-guaranteed state estimation for nonlinear delayed systems under mixed attacks. International Journal of Systems Science, 54(9), 2059-2071. · Zbl 1520.93561
[124] Ying, C., Zheng, N., Wu, Y., Xu, M., & Zhang, W.-A. (2023). Privacy-preserving adaptive resilient consensus for multi-agent systems under cyber attacks. IEEE Transactions on Industrial Informatics, 20(2), 1630-1640.
[125] Ying, Z., Cao, S., Liu, X., Ma, Z., Ma, J., & Deng, R. H. (2022). Privacysignal: Privacy-preserving traffic signal control for intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16290-16303.
[126] Yue, J., Qin, K., Shi, M., Jiang, B., Li, W., & Shi, L. (2023). Event-trigger-based finite-time privacy-preserving formation control for multi-UAV system. Drones, 7(4), 235.
[127] Zhang, D., & Ni, Y. (2023). Differential privacy optimal control with asymmetric information structure. Optimal Control Applications and Methods, 45(1), 393-412. · Zbl 1531.93021
[128] Zhang, J., Lu, J., & Chen, X. (2022). Privacy-preserving average consensus via edge decomposition. IEEE Control Systems Letters, 6, 2503-2508.
[129] Zhang, J., Lu, J., & Lou, J. (2022). Privacy-preserving average consensus via finite time-varying transformation. IEEE Transactions on Network Science and Engineering, 9(3), 1756-1764.
[130] Zhang, J., Tan, J., & Wang, J. (2021). Privacy security in control systems. Science China Information Sciences, 64(7). Article 176201.
[131] Zhang, K., Li, Z., Wang, Y., Louati, A., & Chen, J. (2022). Privacy-preserving dynamic average consensus via state decomposition: Case study on multi-robot formation control. Automatica, 139. Article 110182. · Zbl 1485.93548
[132] Zhang, Q., Sun, W., & Qiao, C. (2023). Event-triggered stabilisation of switched nonlinear systems with actuator saturation: A hamiltonian approach. International Journal of Systems Science, 54(4), 849-866. · Zbl 1520.93335
[133] Zhang, W., Zuo, Z., & Hu, G. (2023). How much noise suffices for privacy of multiagent systems?IEEE Transactions on Automatic Control, 68(10), 6051-6066. · Zbl 07794451
[134] Zhang, X., Song, J., Cheng, P., Shi, K., & He, S. (2023). Mean square exponential stabilisation for directional 2D Roesser hidden Markov model. International Journal of Systems Science, 54(4), 867-879. · Zbl 1520.93602
[135] Zhang, Y., Peng, Z., Wen, G., Wang, J., & Huang, T. (2023). Privacy preserving-based resilient consensus for multiagent systems via state decomposition. IEEE Transactions on Control of Network Systems, 10(3), 1172-1183.
[136] Zhao, D., Liu, D., & Liu, L. (2023). Distributed and privacy preserving MPC with global constraints over time-varying communication. IEEE Transactions on Control of Network Systems, 10(2), 586-598.
[137] Zhao, S., Li, F., Li, H., Lu, R., Ren, S., Bao, H., Lin, J., & Han, S. (2021). Smart and practical privacy-preserving data aggregation for fog-based smart grids. IEEE Transactions on Information Forensics and Security, 16, 521-536.
[138] Zhao, Y., He, X., Ma, L., & Liu, H. (2022). Unbiasedness-constrained least squares state estimation for time-varying systems with missing measurements under round-robin protocol. International Journal of Systems Science, 53(9), 1925-1941. · Zbl 1492.93184
[139] Zhao, Z., Xia, L., Jiang, L., Ge, Q., & Yu, F. (2023). Distributed bandit online optimisation for energy management in smart grids. International Journal of Systems Science, 54(16), 2957-2974. · Zbl 1534.93155
[140] Zheng, T., Zhou, Y., Hu, M., & Zhang, J. (2023). Dynamic scheduling for large-scale flexible job shop based on noisy DDQN. International Journal of Network Dynamics and Intelligence, 2(4). Article 100015.
[141] Zhu, X., Ayday, E., & Vitenberg, R. (2021). A privacy-preserving framework for outsourcing location-based services to the cloud. IEEE Transactions on Dependable and Secure Computing, 18(1), 384-399.
[142] Zhu, Y., Lu, J., Du, X., & Guo, G. (2023). Fake information mechanism based privacy-preserving average consensus. International Journal of Systems Science, 54(8), 1803-1814. · Zbl 1520.93532
[143] Zou, L., Wang, Z., Shen, B., & Dong, H. (2023). Encryption-decryption-based state estimation with multirate measurements against eavesdroppers: A recursive minimum-variance approach. IEEE Transactions on Automatic Control, 68(12), 8111-8118. · Zbl 07811043
[144] Zou, L., Wang, Z., Shen, B., Dong, H., & Lu, G. (2023). Encrypted finite-horizon energy-to-peak state estimation for time-varying systems under eavesdropping attacks: Tackling secrecy capacity. IEEE/CAA Journal of Automatica Sinica, 10(4), 985-996.
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