دوره 17، شماره 2 - ( مجله کنترل، جلد 17، شماره 2، تابستان 1402 )                   جلد 17 شماره 2,1402 صفحات 194-179 | برگشت به فهرست نسخه ها

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Adelipour S, Haeri M. A Review of Privacy Preserving Encrypted Control for Cyber-Physical Systems. JoC 2023; 17 (2) :179-194
URL: http://joc.kntu.ac.ir/article-1-997-fa.html
عادلی پور سعید، حائری محمد. مروری بر روش‌های کنترل رمزنگاری شده برای حفظ حریم خصوصی در سیستم‌های سایبرفیزیکی. مجله کنترل. 1402; 17 (2) :179-194

URL: http://joc.kntu.ac.ir/article-1-997-fa.html


1- دانشکده مهندسی برق، گروه کنترل، دانشگاه صنعتی شریف،تهران، ایران
چکیده:   (1626 مشاهده)
بهره‌گیری از مفاهیم رایانش ابری و محاسبات توزیع‌یافته، مزایای متنوعی نظیر عملکرد بهتر، امکان برون‌سپاری محاسبات پیچیده و مقیاس‌پذیری سریع را در بسیاری از سیستم‌های کنترل شبکه‌ای مانند شبکه‌های هوشمند انرژی، ساختمان‌های هوشمند، حمل و نقل هوشمند و ... ایجاد کرده است. از طرف دیگر، خطر افشا شدن اطلاعات مهم، دست‌کاری شدن آن‌ها توسط عوامل خارجی و کاهش اعتماد عمومی به روش‌های کنترل غیرمتمرکز و توزیع‌یافته که در آن‌ عامل‌ها ممکن است به دلایل مختلف مایل به اشتراک‌گذاری اطلاعات نباشند، از مهم‌ترین چالش‌های موجود در کنترل سیستم‌های سایبرفیزیکی است. این مقاله به مرور روش‌های کنترل رمزنگاری شده، که با حفظ حریم خصوصی به برخی از این چالش‌ها پاسخ می‌دهند، می‌پردازد. در این روش‌ها، محاسبات مورد نیاز به طور مستقیم بر روی سیگنال‌های رمزنگاری شده انجام می‌شود و نیازی به باز کردن رمز و در معرض خطر قرار دادن اطلاعات مهم وجود ندارد. این کار امکان دسترسی حمله‌کننده‌ها به اطلاعات حیاتی سیستم کنترلی را بسیار محدود می‌کند و از آن‌جایی که برای طراحی حملات پیچیده‌تر عموماً به اطلاعات به دست آمده از سیستم نیاز است، حفظ خصوصی بودن سیگنال‌ها در تمام حلقه‌ی کنترل احتمال طرح حمله‌های سایبری پیچیده‌تر را نیز به طور قابل ملاحظه‌ای کاهش می‌دهد. از این رو در این مقاله، رمزنگاری هم‌ریختی و محاسبات چندجانبه‌ای امن به عنوان پایه‌های حفظ حریم خصوصی و ایجاد روش‌های کنترلی امن معرفی شده و روش‌های کنترل و بهینه‌سازی توسعه یافته بر مبنای آن‌ها مرور می‌شوند. کاستی‌ها و چالش‌های روش‌های موجود بحث شده و مسیر آینده‌ی تحقیقات در این رویکرد نوظهور در مهندسی کنترل ترسیم می‌شود.
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نوع مطالعه: پژوهشي | موضوع مقاله: شماره ویژه (رویکرد های نو در مهندسی کنترل)
دریافت: 1402/5/18 | پذیرش: 1402/6/17 | انتشار الکترونیک پیش از انتشار نهایی: 1402/6/20 | انتشار: 1402/6/30

فهرست منابع
1. [1] Xia, Y., Zhang, Y., Dai, L., Zhan, Y., & Guo, Z. (2022). A brief survey on recent advances in cloud control systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(7), 3108-3114. [DOI:10.1109/TCSII.2022.3178975]
2. [2] Zhang, D., Wang, Q.G., Feng, G., Shi, Y., & Vasilakos, A.V. (2021). A survey on attack detection, estimation and control of industrial cyber-physical systems. ISA transactions, 116, 1-16. [DOI:10.1016/j.isatra.2021.01.036]
3. [3] Dibaji, S.M., Pirani, M., Flamholz, D.B., Annaswamy, A.M., Johansson, K.H., & Chakrabortty, A. (2019). A systems and control perspective of CPS security. Annual Reviews in Control, 47, 394-411. [DOI:10.1016/j.arcontrol.2019.04.011]
4. [4] Sandberg, H., Gupta, V., & Johansson, K.H. (2022). Secure networked control systems. Annual Review of Control, Robotics, and Autonomous Systems, 5, 445-464. [DOI:10.1146/annurev-control-072921-075953]
5. [5] Teixeira, A., Sou, K.C., Sandberg, H., & Johansson, K.H. (2015). Secure control systems: A quantitative risk management approach. IEEE Control Systems Magazine, 35(1), 24-45. [DOI:10.1109/MCS.2014.2364709]
6. [6] 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. [DOI:10.1016/j.arcontrol.2019.04.006]
7. [7] 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. [DOI:10.1016/j.arcontrol.2019.04.010]
8. [8] Sánchez, H. S., Rotondo, D., Escobet, T., Puig, V., & Quevedo, J. (2019). Bibliographical review on cyber-attacks from a control-oriented perspective. Annual Reviews in Control, 48, 103-128. [DOI:10.1016/j.arcontrol.2019.08.002]
9. [9] Li, G., Ren, L., Fu, Y., Yang, Z., Adetola, V., Wen, J., Zhu, Q., Wu, T., Candan, K.S. & O'Neill, Z. (2023). A critical review of cyber-physical security for building automation systems. Annual Reviews in Control, 55, 237-254. [DOI:10.1016/j.arcontrol.2023.02.004]
10. [10] Arauz, T., Chanfreut, P., & Maestre, J. M. (2022). Cyber-security in networked and distributed model predictive control. Annual Reviews in Control, 53, 338-355. [DOI:10.1016/j.arcontrol.2021.10.005]
11. [11] Blanco-Justicia, A., Domingo-Ferrer, J., Martínez, S., Sánchez, D., Flanagan, A., & Tan, K. E. (2021). Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Engineering Applications of Artificial Intelligence, 106, 104468. [DOI:10.1016/j.engappai.2021.104468]
12. [12] Afshar, A., Termehchy, A., Golshan, A., Aghaeeyan, A., & Shahriyari, H. (2014). Survey on cyber security of industrial control systems. Journal of Control, 8(1), 31-45.
13. [13] Cheng, Z., Ye, F., Cao, X., & Chow, M.Y. (2021). A homomorphic encryption-based private collaborative distributed energy management system. IEEE Transactions on Smart Grid, 12(6), 5233-5243. [DOI:10.1109/TSG.2021.3091624]
14. [14] Zhang, C. & Wang, Y. (2018). Enabling privacy-preservation in decentralized optimization. IEEE Transactions on Control of Network Systems, 6(2), 679-689. [DOI:10.1109/TCNS.2018.2873152]
15. [15] Huo, X. & Liu, M. (2021). Encrypted decentralized multi-agent optimization for privacy preservation in cyber-physical systems. IEEE Transactions on Industrial Informatics. In Press.
16. [16] Sharma, S. & Kaushik, B. (2019). A survey on internet of vehicles: Applications, security issues & solutions. Vehicular Communications, 20, 100182. [DOI:10.1016/j.vehcom.2019.100182]
17. [17] Sultangazin, A. & Tabuada, P. (2020). Symmetries and isomorphisms for privacy in control over the cloud. IEEE Transactions on Automatic Control, 66(2), 538-549. [DOI:10.1109/TAC.2020.2982611]
18. [18] Darup, M.S., Alexandru, A.B., Quevedo, D.E., & Pappas, G.J. (2021). Encrypted Control for Networked Systems: An Illustrative Introduction and Current Challenges. IEEE Control Systems Magazine, 41(3), 58-78. [DOI:10.1109/MCS.2021.3062956]
19. [19] Suryavanshi, A., Alnajdi, A., Alhajeri, M., Abdullah, F., & Christofides, P. D. (2023). Encrypted model predictive control design for security to cyberattacks. AIChE Journal, 69(8), e18104. [DOI:10.1002/aic.18104]
20. [20] Sun, Q., & Shi, Y. (2021). Model predictive control as a secure service for cyber-physical systems: A cloud-edge framework. IEEE Internet of Things Journal, 9(22), 22194-22203. [DOI:10.1109/JIOT.2021.3091981]
21. [21] Kim, J., Kim, D., Song, Y., Shim, H., Sandberg, H., & Johansson, K.H. (2022). Comparison of encrypted control approaches and tutorial on dynamic systems using Learning With Errors-based homomorphic encryption. Annual Reviews in Control, 54, 200-218. [DOI:10.1016/j.arcontrol.2022.10.002]
22. [22] Umsonst, D. & Sandberg, H. (2021). On the confidentiality of controller states under sensor attacks. Automatica, 123, 109329. [DOI:10.1016/j.automatica.2020.109329]
23. [23] An, L., & Yang, G.H. (2022). Enhancement of opacity for distributed state estimation in cyber-physical systems. Automatica, 136, 110087. [DOI:10.1016/j.automatica.2021.110087]
24. [24] Wang, L., Zhang, M., Zhu, J., Xing, L., & Wu, Q. (2022). A privacy-preserving decentralized randomized block-coordinate subgradient algorithm over time-varying networks. Expert Systems with Applications, 208, 118099. [DOI:10.1016/j.eswa.2022.118099]
25. [25] Murguia, C., Shames, I., Farokhi, F., Nešić, D., & Poor, H.V. (2021). On privacy of dynamical systems: An optimal probabilistic mapping approach. IEEE Transactions on Information Forensics and Security, 16, 2608-2620. [DOI:10.1109/TIFS.2021.3055022]
26. [26] Hassan, M.U., Rehmani, M.H., & Chen, J. (2019). Differential privacy techniques for cyber physical systems: a survey. IEEE Communications Surveys & Tutorials, 22(1), 746-789. [DOI:10.1109/COMST.2019.2944748]
27. [27] Wang, Y., & Nedić, A. (2023). Tailoring gradient methods for differentially-private distributed optimization. IEEE Transactions on Automatic Control, In press. [DOI:10.1109/TAC.2023.3272968]
28. [28] Chen, B., Leahy, K., Jones, A., & Hale, M. (2023). Differential privacy for symbolic systems with application to Markov Chains. Automatica, 152, 110908. [DOI:10.1016/j.automatica.2023.110908]
29. [29] Huo, X., & Liu, M. (2021). Privacy-preserving distributed multi-agent cooperative optimization-paradigm design and privacy analysis. IEEE Control Systems Letters, 6, 824-829. [DOI:10.1109/LCSYS.2021.3086441]
30. [30] Farokhi, F., & Esfahani, P. M. (2018, December). Security versus privacy. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 7101-7106). IEEE. [DOI:10.1109/CDC.2018.8619460]
31. [31] Chong, M. S., Sandberg, H., & Teixeira, A. M. (2019, June). A tutorial introduction to security and privacy for cyber-physical systems. In 2019 18th European Control Conference (ECC) (pp. 968-978). IEEE. [DOI:10.23919/ECC.2019.8795652]
32. [32] Liu, S., Trivedi, A., Yin, X., & Zamani, M. (2022). Secure-by-construction synthesis of cyber-physical systems. Annual Reviews in Control, 53, 30-50. [DOI:10.1016/j.arcontrol.2022.03.004]
33. [33] Ding, D., Han, Q.L., Xiang, Y., Ge, X., & Zhang, X. M. (2018). A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing, 275, 1674-1683. [DOI:10.1016/j.neucom.2017.10.009]
34. [34] Weerakkody, S., Ozel, O., Mo, Y., & Sinopoli, B. (2019). Resilient control in cyber-physical systems: Countering uncertainty, constraints, and adversarial behavior. Foundations and Trends® in Systems and Control, 7(1-2), 1-252. [DOI:10.1561/9781680835878]
35. [35] Kordestani, M., & Saif, M. (2021). Observer-based attack detection and mitigation for cyberphysical systems: A review. IEEE Systems, Man, and Cybernetics Magazine, 7(2), 35-60. [DOI:10.1109/MSMC.2020.3049092]
36. [36] Duo, W., Zhou, M., & Abusorrah, A. (2022). A survey of cyber-attacks on cyber physical systems: Recent advances and challenges. IEEE/CAA Journal of Automatica Sinica, 9(5), 784-800. [DOI:10.1109/JAS.2022.105548]
37. [37] Burbano, L., Garg, K., Leudo, S. J., Cardenas, A. A., & Sanfelice, R. G. (2023). Online attack recovery in cyberphysical systems. IEEE Security & Privacy, 21(4), 20-28. [DOI:10.1109/MSEC.2023.3268573]
38. [38] Tran, J., Farokhi, F., Cantoni, M., & Shames, I. (2020). Implementing homomorphic encryption based secure feedback control. Control Engineering Practice, 97, 104350. [DOI:10.1016/j.conengprac.2020.104350]
39. [39] ElGamal, T. (1985). A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE transactions on information theory, 31(4), 469-472. [DOI:10.1109/TIT.1985.1057074]
40. [40] Paillier, P. (1999, May). Public-key cryptosystems based on composite degree residuosity classes. In International conference on the theory and applications of cryptographic techniques, (pp. 223-238). Springer, Berlin, Heidelberg. [DOI:10.1007/3-540-48910-X_16]
41. [41] Gentry, C. (2009, May). Fully homomorphic encryption using ideal lattices. In Proceedings of the forty-first annual ACM symposium on Theory of computing, (pp. 169-178). [DOI:10.1145/1536414.1536440]
42. [42] Teranishi, K., Sadamoto, T., & Kogiso, K. (2023). Input-output history feedback controller for encrypted control with leveled fully homomorphic encryption. IEEE Transactions on Control of Network Systems, In press. [DOI:10.1109/TCNS.2023.3280460]
43. [43] Cheon, J.H., Kim, A., Kim, M., & Song, Y. (2017). Homomorphic encryption for arithmetic of approximate numbers. In 23rd International Conference on the Theory and Applications of Cryptology and Information Security, Hong Kong, China, (pp. 409-437). Springer. [DOI:10.1007/978-3-319-70694-8_15]
44. [44] Ducas, L., & Micciancio, D. (2015, April). FHEW: bootstrapping homomorphic encryption in less than a second. In Annual international conference on the theory and applications of cryptographic techniques (pp. 617-640). Berlin, Heidelberg: Springer. [DOI:10.1007/978-3-662-46800-5_24]
45. [45] Shamir, A. (1979). How to share a secret. Communications of the ACM, 22(11), 612-613. [DOI:10.1145/359168.359176]
46. [46] Xia, Z., Gu, Q., Zhou, W., Xiong, L., Weng, J., & Xiong, N. (2021). STR: Secure computation on additive shares using the share-transform-reveal strategy. IEEE Transactions on Computers, In press.
47. [47] Tjell, K. & Wisniewski, R. (2021). Privacy in Distributed Computations based on Real Number Secret Sharing. arXiv preprint arXiv:2107.00911.
48. [48] Zhao, C., Zhao, S., Zhao, M., Chen, Z., Gao, C. Z., Li, H., & Tan, Y.A. (2019). Secure multi-party computation: theory, practice and applications. Information Sciences, 476, 357-372. [DOI:10.1016/j.ins.2018.10.024]
49. [49] Darup, M.S. & Jager, T. (2019, December). Encrypted cloud-based control using secret sharing with one-time pads. In 2019 IEEE 58th Conference on Decision and Control (CDC), (pp. 7215-7221). [DOI:10.1109/CDC40024.2019.9029342]
50. [50] Farokhi, F., Shames, I., & Batterham, N. (2017). Secure and private control using semi-homomorphic encryption. Control Engineering Practice, 67, 13-20. [DOI:10.1016/j.conengprac.2017.07.004]
51. [51] Kogiso, K. & Fujita, T. (2015, December). Cyber-security enhancement of networked control systems using homomorphic encryption. In 2015 54th IEEE Conference on Decision and Control (CDC), (pp. 6836-6843). [DOI:10.1109/CDC.2015.7403296]
52. [52] Teranishi, K., Shimada, N., & Kogiso, K. (2020). Stability-guaranteed dynamic ElGamal cryptosystem for encrypted control systems. IET Control Theory & Applications, 14(16), 2242-2252. [DOI:10.1049/iet-cta.2019.0729]
53. [53] Farokhi, F., Shames, I., & Batterham, N. (2016). Secure and private cloud-based control using semi-homomorphic encryption. IFAC-PapersOnLine, 49(22), 163-168. [DOI:10.1016/j.ifacol.2016.10.390]
54. [54] Darup, M.S. (2020). Encrypted polynomial control based on tailored two‐party computation. International Journal of Robust and Nonlinear Control, 30(11), 4168-4187. [DOI:10.1002/rnc.5003]
55. [55] Schlor, S., Hertneck, M., Wildhagen, S., & Allgöwer, F. (2021, December). Multi-party computation enables secure polynomial control based solely on secret-sharing. In 2021 60th IEEE conference on decision and control (CDC) (pp. 4882-4887). IEEE. [DOI:10.1109/CDC45484.2021.9683026]
56. [56] Murguia, C., Farokhi, F., & Shames, I. (2020). Secure and private implementation of dynamic controllers using semihomomorphic encryption. IEEE Transactions on Automatic Control, 65(9), 3950-3957. [DOI:10.1109/TAC.2020.2992445]
57. [57] Cheon, J. H., Han, K., Kim, H., Kim, J., & Shim, H. (2018, December). Need for controllers having integer coefficients in homomorphically encrypted dynamic system. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 5020-5025). IEEE. [DOI:10.1109/CDC.2018.8619600]
58. [58] Schlüter, N., & Darup, M. S. (2021). On the stability of linear dynamic controllers with integer coefficients. IEEE Transactions on Automatic Control, 67(10), 5610-5613. [DOI:10.1109/TAC.2021.3131126]
59. [59] Tavazoei, M.S. (2022). Non-minimality of the realizations and possessing state matrices with integer elements in linear discrete-time controllers. IEEE Transactions on Automatic Control, 68(6), 3698-3703. [DOI:10.1109/TAC.2022.3192811]
60. [60] Tavazoei, M.S. (2023). Pisot number-based discrete-time controllers with integer state matrices to ensure monotonic closed-loop step responses. IEEE Transactions on Automatic Control, In press. [DOI:10.1109/TAC.2023.3292177]
61. [61] Kim, J., Shim, H., & Han, K. (2022). Dynamic controller that operates over homomorphically encrypted data for infinite time horizon. IEEE Transactions on Automatic Control, 68(2), 660-672. [DOI:10.1109/TAC.2022.3142124]
62. [62] Darup, M.S., Redder, A., Shames, I., Farokhi, F., & Quevedo, D. (2017). Towards encrypted MPC for linear constrained systems. IEEE Control Systems Letters, 2(2), 195-200. [DOI:10.1109/LCSYS.2017.2779473]
63. [63] Schlüter, N. & Darup, M.S. (2020, December). Encrypted explicit MPC based on two-party computation and convex controller decomposition. In 2020 59th IEEE Conference on Decision and Control (CDC), 5469-5476. [DOI:10.1109/CDC42340.2020.9304078]
64. [64] Alexandru, A.B., Morari, M., & Pappas, G.J. (2018, December). Cloud-based MPC with encrypted data. In 2018 IEEE Conference on Decision and Control (CDC), 5014-5019. [DOI:10.1109/CDC.2018.8619835]
65. [65] Darup, M. S., Redder, A., & Quevedo, D. E. (2018). Encrypted cloud-based MPC for linear systems with input constraints. IFAC-PapersOnLine, 51(20), 535-542. [DOI:10.1016/j.ifacol.2018.11.035]
66. [66] Darup, M. S. (2020). Encrypted MPC based on ADMM real-time iterations. IFAC-PapersOnLine, 53(2), 3508-3514. [DOI:10.1016/j.ifacol.2020.12.1708]
67. [67] Alexandru, A.B., Gatsis, K., Shoukry, Y., Seshia, S.A., Tabuada, P., & Pappas, G.J. (2020). Cloud-based quadratic optimization with partially homomorphic encryption. IEEE Transactions on Automatic Control, 66(5), 2357-2364. [DOI:10.1109/TAC.2020.3005920]
68. [68] Zhang, Z., Che, X., Jiao, X., Yu, W., & Wan, L. (2022, May). Quadratic Optimization Using Additive Homomorphic Encryption in CPS. In 2022 13th Asian Control Conference (ASCC) (pp. 1995-2000). IEEE. [DOI:10.23919/ASCC56756.2022.9828190]
69. [69] Yang, Z., Zhang, Z., & Tian, Y. (2022, May). Experimental Validation of Encrypted Quadratic Optimization Implemented on Raspberry Pi. In 2022 13th Asian Control Conference (ASCC) (pp. 2018-2023). IEEE. [DOI:10.23919/ASCC56756.2022.9828260]
70. [70] Adelipour, S. & Haeri, M. (2023, May) Privacy-preserving model predictive control using secure multi-party computation, In 2023 31st International Conference on Electrical Engineering (ICEE) (pp. 915-919). IEEE. [DOI:10.1109/ICEE59167.2023.10334878]
71. [71] Tjell, K., & Wisniewski, R. (2019, December). Privacy preservation in distributed optimization via dual decomposition and ADMM. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 7203-7208). IEEE. [DOI:10.1109/CDC40024.2019.9028969]
72. [72] Tian, N., Guo, Q., Sun, H., & Zhou, X. (2022). Fully privacy-preserving distributed optimization in power systems based on secret sharing. Energy, 1(3), 351-362. [DOI:10.23919/IEN.2022.0045]
73. [73] Hossein ali zadeh, T., Turkmen, F., & Monshizadeh, N. (2022). Private computation of polynomials over networks. Systems & Control Letters, 166, 105291. [DOI:10.1016/j.sysconle.2022.105291]
74. [74] Tjell, K. & Wisniewski, R. (2020). Privacy preserving distributed summation in a connected graph. IFAC-PapersOnLine, 53(2), 3445-3450. [DOI:10.1016/j.ifacol.2020.12.1677]
75. [75] Darup, M.S., Redder, A., & Quevedo, D.E. (2018). Encrypted cooperative control based on structured feedback. IEEE control systems letters, 3(1), 37-42. [DOI:10.1109/LCSYS.2018.2851152]
76. [76] Lu, Y., & Zhu, M. (2018). Privacy preserving distributed optimization using homomorphic encryption. Automatica, 96, 314-325. [DOI:10.1016/j.automatica.2018.07.005]
77. [77] Wu, T., Zhao, C., & Zhang, Y.J.A. (2021). Privacy-preserving distributed optimal power flow with partially homomorphic encryption. IEEE Transactions on Smart Grid, 12(5), 4506-4521. [DOI:10.1109/TSG.2021.3084934]
78. [78] Huo, X. & Liu, M. (2022). Distributed privacy-preserving electric vehicle charging control based on secret sharing. Electric Power Systems Research, 211, 108357. [DOI:10.1016/j.epsr.2022.108357]
79. [85] Fang, W., Zamani, M., & Chen, Z. (2021). Secure and privacy preserving consensus for second-order systems based on Paillier encryption. Systems & Control Letters, 148, 104869. [DOI:10.1016/j.sysconle.2020.104869]
80. [86] Zhang, Z., Cheng, P., Wu, J., & Chen, J. (2020). Secure State Estimation Using Hybrid Homomorphic Encryption Scheme. IEEE Transactions on Control Systems Technology, 29(4), 1704-1720. [DOI:10.1109/TCST.2020.3019501]
81. [81] Sadeghikhorami, L., Zamani, M., Chen, Z., & Safavi, A.A. (2020). A secure control mechanism for network environments. Journal of the Franklin Institute, 357(17), 12264-12280. [DOI:10.1016/j.jfranklin.2020.09.010]
82. [82] Sadeghikhorami, L., Varadharajan, V., & Safavi, A.A. (2021). A novel secure observer-based controller and attack detection scheme for Networked Control Systems. Information Sciences, 575, 185-205. [DOI:10.1016/j.ins.2021.06.012]
83. [83] Sadeghikhorami, L. & Safavi, A.A. (2021). Secure distributed Kalman filter using partially homomorphic encryption. Journal of the Franklin Institute, 358(5), 2801-2825. [DOI:10.1016/j.jfranklin.2020.08.048]
84. [84] 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, 100786. [DOI:10.1016/j.ejcon.2023.100786]
85. [85] Feng, Z., Cao, G., Grigoriadis, K.M., & Pan, Q. (2023). Secure MPC-based Path-Following for UAS in Adverse Network Environment. IEEE Transactions on Industrial Informatics, In press. [DOI:10.1109/TII.2022.3232772]
86. [86] Solnør, P., Petrovic, S., & Fossen, T. I. (2023). Towards Oblivious Guidance Systems for Autonomous Vehicles. IEEE Transactions on Vehicular Technology, 72(6), 7067-7081. [DOI:10.1109/TVT.2023.3237892]
87. [87] Hassija, V., Chamola, V., Bajpai, B.C., & Zeadally, S. (2021). Security issues in implantable medical devices: Fact or fiction?. Sustainable Cities and Society, 66, 102552. [DOI:10.1016/j.scs.2020.102552]
88. [88] Torkzadehmahani, R., Nasirigerdeh, R., Blumenthal, D.B., Kacprowski, T., List, M., Matschinske, J., & Baumbach, J. (2022). Privacy-preserving artificial intelligence techniques in biomedicine. Methods of Information in Medicine, 61, e12-e27. [DOI:10.1055/s-0041-1740630]
89. [89] Weng, H., Hettiarachchi, C., Nolan, C., Suominen, H., & Lenskiy, A. (2023). Ensuring security of artificial pancreas device system using homomorphic encryption. Biomedical Signal Processing and Control, 79, 104044. [DOI:10.1016/j.bspc.2022.104044]
90. [90] 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. [DOI:10.1109/TITS.2022.3149600]
91. [91] Kang, H.E.D., Kim, D., Kim, S., Kim, D.D., Cheon, J.H., & Anthony, B.W. (2021). Homomorphic encryption as a secure PHM outsourcing solution for small and medium manufacturing enterprise. Journal of Manufacturing Systems, 61, 856-865. [DOI:10.1016/j.jmsy.2021.06.001]
92. [92] Kogiso, K. (2018, December). Attack detection and prevention for encrypted control systems by application of switching-key management. In 2018 IEEE Conference on Decision and Control (CDC), (pp. 5032-5037). [DOI:10.1109/CDC.2018.8619221]
93. [93] Kawano, Y., Kashima, K., & Cao, M. (2021). Modular control under privacy protection: Fundamental trade-offs. Automatica, 127, 109518. [DOI:10.1016/j.automatica.2021.109518]
94. [94] Kogiso, K. (2018, June). Upper-bound analysis of performance degradation in encrypted control system. In 2018 Annual American Control Conference (ACC), (pp. 1250-1255). [DOI:10.23919/ACC.2018.8431234]
95. [95] Teranishi, K. & Kogiso, K. (2021). ElGamal-type encryption for optimal dynamic quantizer in encrypted control systems. SICE Journal of Control, Measurement, and System Integration, 14(1), 59-66. [DOI:10.1080/18824889.2021.1906016]
96. [96] Soleymani, M., Mahdavifar, H., & Avestimehr, A. S. (2022). Analog secret sharing with applications to private distributed learning. IEEE Transactions on Information Forensics and Security, 17, 1893-1904. [DOI:10.1109/TIFS.2022.3173417]
97. [97] Teranishi, K., Ueda, J., & Kogiso, K. (2020). Event-triggered approach to increasing sampling period of encrypted control systems. IFAC-PapersOnLine, 53(2), 3502-3507. [DOI:10.1016/j.ifacol.2020.12.1705]
98. [98] Damgård, I., Geisler, M., Krøigaard, M., & Nielsen, J. B. (2009, March). Asynchronous multiparty computation: Theory and implementation. In International workshop on public key cryptography (pp. 160-179). Berlin, Heidelberg: Springer. [DOI:10.1007/978-3-642-00468-1_10]
99. [99] Fauser, M., & Zhang, P. (2021, December). Resilient homomorphic encryption scheme for cyber-physical systems. In 2021 60th IEEE Conference on Decision and Control (CDC) (pp. 5634-5639). IEEE. [DOI:10.1109/CDC45484.2021.9683696]
100. [100] Fauser, M., & Zhang, P. (2022, June). Detection of cyber-attacks in encrypted control systems. In 2022 American Control Conference (ACC) (pp. 4992-4997). IEEE. [DOI:10.23919/ACC53348.2022.9867248]
101. [101] Miyamoto, M., Teranishi, K., Emura, K., & Kogiso, K. (2023). Cybersecurity-Enhanced Encrypted Control System Using Keyed-Homomorphic Public Key Encryption. IEEE Access, 11, 45749-45760. [DOI:10.1109/ACCESS.2023.3274691]
102. [102] Naseri, A.M., Lucia, W., & Youssef, A. (2023). Confidentiality attacks against encrypted control systems. Cyber-Physical Systems, 9(3), 224-243. [DOI:10.1080/23335777.2022.2051209]

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