This paper explores the use of differential privacy encryption to protect against data tampering attacks in the context of finite impulse response (FIR) system identification under binary observation conditions. The study begins by introducing the core principles of differential privacy and discussing the current security challenges faced by FIR systems. It highlights the risks of data tampering and privacy leakage during the system identification process. To address these challenges, two distinct differential privacy algorithms are proposed, providing dual encryption protection for the system parameters. By integrating differential privacy mechanisms into the FIR system, the proposed approach ensures the security and privacy of both data and parameters during transmission and processing. Experimental results demonstrate that the dual differential privacy protection effectively safeguards data while providing accurate parameter estimation, validating the effectiveness of the proposed scheme.
Citation: Bochen Li, Ting Wang. Identification of a FIR system with binary-valued observation under data tampering attack and differential privacy preservation[J]. Electronic Research Archive, 2025, 33(6): 3989-4013. doi: 10.3934/era.2025177
This paper explores the use of differential privacy encryption to protect against data tampering attacks in the context of finite impulse response (FIR) system identification under binary observation conditions. The study begins by introducing the core principles of differential privacy and discussing the current security challenges faced by FIR systems. It highlights the risks of data tampering and privacy leakage during the system identification process. To address these challenges, two distinct differential privacy algorithms are proposed, providing dual encryption protection for the system parameters. By integrating differential privacy mechanisms into the FIR system, the proposed approach ensures the security and privacy of both data and parameters during transmission and processing. Experimental results demonstrate that the dual differential privacy protection effectively safeguards data while providing accurate parameter estimation, validating the effectiveness of the proposed scheme.
| [1] | C. Dwork, A. Roth, The Algorithmic Foundations of Differential Privacy, Now Publishers Inc., 2014. http://dx.doi.org/10.1561/0400000042 |
| [2] |
Y. Kim, R. Eum, S. Park, Stealthy sensor attack detection and real-time performance recovery for resilient CPS, IEEE Trans. Ind. Inf., 17 (2021), 7412–7422. http://dx.doi.org/10.1109/TII.2021.3052182 doi: 10.1109/TII.2021.3052182
|
| [3] |
J. Guo, Q. Zhang, Y. Zhao, Identification of FIR Systems with binary-valued observations under replay attacks, Automatica, 172 (2025), 112001. https://doi.org/10.1016/j.automatica.2024.112001 doi: 10.1016/j.automatica.2024.112001
|
| [4] |
Y. Jiang, S. Wu, H. Yang, H. Luo, Z. Chen, S. Yin, et al., Secure data transmission and trustworthiness judgement approaches against cyber-physical attacks in an integrated data-driven framework, IEEE Trans. Syst. Man Cybern.: Syst., 52 (2022), 7799–7809. https://doi.org/10.1109/TSMC.2022.3164024 doi: 10.1109/TSMC.2022.3164024
|
| [5] | J. Glavaš, I. Uroda, B. Mandić, Managing digital transformation in public administration, in 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), (2021), 1466–1469. https://doi.org/10.23919/MIPRO52101.2021.9596775 |
| [6] | H. Wang, L. Wang, Y. Yang, M. Hu, Z. Jia, Z. Chen, et al., A secure and efficient public data auditing solution for the cloud, in 2024 16th International Conference on Communication Software and Networks (ICCSN), (2024), 28–32. https://doi.org/10.1109/ICCSN63464.2024.10793330 |
| [7] | S. Saratkar, A. Chaudhari, T. Thute, R. Raut, G. Thakre, H. Kumar, Assessment of heart-attack prediction using fuzzy rule based system, in 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), (2024), 1–6. https://doi.org/10.1109/ICCUBEA61740.2024.10774808 |
| [8] | D. B. Rawat, J. J. P. C. Rodrigues, I. Stojmenovic, Cyber-Physical Systems: From Theory to Practice, CRC Press, 2015. |
| [9] | L. Ljung, System Identification: Theory for the User, 2$^{nd}$ edition, Prentice Hall, 1999. |
| [10] |
M. Pouliquen, E. Pigeon, O. Gehan, A. Goudjil, Identification using binary measurements for IIR systems, IEEE Trans. Autom. Control, 65 (2020), 786–793. https://doi.org/10.1109/TAC.2019.2921657 doi: 10.1109/TAC.2019.2921657
|
| [11] |
J. Guo, J. F. Zhang, Y. Zhao, Adaptive tracking of a class of first-order systems with binary-valued observations and fixed thresholds, J. Syst. Sci. Complexity, 25 (2012), 1041–1051. https://doi.org/10.1007/s11424-012-1257-0 doi: 10.1007/s11424-012-1257-0
|
| [12] |
T. Wang, X. Zhang, J. Feng, X. Yang, A comprehensive survey on local differential privacy toward data statistics and analysis, Sensors, 20 (2020), 7030. https://doi.org/10.3390/s20247030 doi: 10.3390/s20247030
|
| [13] |
D. Ding, Q. Han, Z. Wang, X. Ge, A survey on model-based distributed control and filtering for industrial cyber-physical systems, IEEE Trans. Ind. Inf., 15 (2019), 2483–2499. https://doi.org/10.1109/TII.2019.2905295 doi: 10.1109/TII.2019.2905295
|
| [14] |
G. P. Liu, Networked learning predictive control of nonlinear cyber physical systems, J. Syst. Sci. Complexity, 33 (2020), 1719–1732. https://doi.org/10.1007/s11424-020-0243-1 doi: 10.1007/s11424-020-0243-1
|
| [15] |
M. S. Mahmoud, M. M. Hamdan, U. A. Baroudi, Modeling and control of cyber-physical systems subject to cyberattacks: A survey of recent advances and challenges, Neurocomputing, 338 (2019), 101–115. https://doi.org/10.1016/j.neucom.2019.01.099 doi: 10.1016/j.neucom.2019.01.099
|
| [16] |
R. Taheri, M. Shojafar, F. Arabikhan, A. Gegov, Unveiling vulnerabilities in deep learning-based malware detection: Differential privacy driven adversarial attacks, Comput. Secur., 146 (2024), 104035. https://doi.org/10.1016/j.cose.2024.104035 doi: 10.1016/j.cose.2024.104035
|
| [17] |
S. Nabavirazavi, R. Taheri, S. S. Iyengar, Enhancing federated learning robustness through randomization and mixture, Future Gener. Comput. Syst., 158 (2024), 28–43. https://doi.org/10.1016/j.future.2024.04.009 doi: 10.1016/j.future.2024.04.009
|
| [18] | R. Taheri, F. Arabikhan, A. Gegov, N. Akbari, Robust aggregation function in federated learning, in International Conference on Information and Knowledge Systems, (2023), 168–175. https://doi.org/10.1007/978-3-031-51664-1_12 |
| [19] |
E. Nowroozi, I. Haider, R. Taheri, M. Conti, Federated learning under attack: Exposing vulnerabilities through data poisoning attacks in computer networks, IEEE Trans. Netw. Serv. Manage., 22 (2025), 822–831. https://doi.org/10.1109/TNSM.2025.3525554 doi: 10.1109/TNSM.2025.3525554
|
| [20] | S. Nabavirazavi, R. Taheri, M. Shojafar, S. S. Iyengar, Impact of aggregation function randomization against model poisoning in federated learning, in 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2023), (2024), 165–172. |
| [21] | R. Tian, J. Mei, Privacy preserving resilient constrained consensus for multi-agent systems via state decomposition, in 2024 43rd Chinese Control Conference (CCC), (2024), 5806–5811. https://doi.org/10.23919/CCC63176.2024.10662823 |
| [22] | J. Cortés, G. E. Dullerud, S. Han, J. Le Ny, S. Mitra, G. J. Pappas, Differential privacy in control and network systems, in 2016 IEEE 55th Conference on Decision and Control (CDC), (2016), 4252–4272. https://doi.org/10.1109/CDC.2016.7798915 |
| [23] | C. Dwork, F. McSherry, K. Nissim, A. Smith, Calibrating noise to sensitivity in private data analysis, in Journal of Privacy and Confidentiality, (2006), 17–51. https://doi.org/10.1007/11681878_14 |
| [24] |
J. Guo, X. Wang, W. Xue, Y. Zhao, System identification with binary-valued observations under data tampering attacks, IEEE Trans. Autom. Control, 66 (2020), 1041–1055. https://doi.org/10.1109/TAC.2020.3029325 doi: 10.1109/TAC.2020.3029325
|
| [25] |
A. Teixeira, I. Shames, H. Sandberg, K. H. Johansson, A secure control framework for resource-limited adversaries, Automatica, 51 (2015), 135–148. https://doi.org/10.1016/j.automatica.2014.10.067 doi: 10.1016/j.automatica.2014.10.067
|
| [26] |
J. Guo, R. Jia, R. Su, Y. Zhao, Identification of FIR systems with binary-valued observations against data tampering attacks, Trans. Syst. Man Cybern.: Syst., 53 (2023), 1041–1055. https://doi.org/10.1109/TSMC.2023.3276352 doi: 10.1109/TSMC.2023.3276352
|
Figures(8) / Tables(1)
Bochen Li, Ting Wang. Identification of a FIR system with binary-valued observation under data tampering attack and differential privacy preservation[J]. Electronic Research Archive, 2025, 33(6): 3989-4013. doi: 10.3934/era.2025177
DownLoad: