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Cloud‐Based Privacy‐Preserving Robust Model Predictive Control Using Semi‐Homomorphic Encryption.

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Title: Cloud‐Based Privacy‐Preserving Robust Model Predictive Control Using Semi‐Homomorphic Encryption.
Authors: Peng, Kai‐Yu1 (AUTHOR), Xie, Wei1 (AUTHOR) weixie@scut.edu.cn, Zhang, Lang‐wen1 (AUTHOR), Mo, Wen‐jun1 (AUTHOR)
Source: International Journal of Robust & Nonlinear Control. Apr2026, Vol. 36 Issue 6, p3198-3209. 12p.
Subjects: Data encryption, Closed loop system stability, Fault-tolerant control systems, Homomorphisms, Data security, Uncertain systems, Mathematical optimization, Feedback control systems
Abstract: In cloud‐based control architectures, a robust model predictive control (RMPC) using semi‐homomorphic encryption is proposed to ensure the stability of polytopic uncertain systems while preserving system privacy and security. Firstly, a cloud‐based encrypted RMPC (ERMPC) framework is proposed, and semi‐homomorphic encryption is utilized to enable encrypted evaluation of control inputs. Secondly, an optimization problem is formulated to address both model uncertainties and quantization‐induced system errors, ensuring closed‐loop stability and invariance through the notion of quadratic boundedness. Thirdly, an ERMPC algorithm is presented, and conditions for selecting encryption‐related parameters are provided to guarantee the stability and bounded control performance of the encrypted cloud‐based control systems. Finally, a numerical example is conducted to demonstrate the effectiveness of the proposed approach in enhancing privacy and security in cloud‐based control systems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In cloud‐based control architectures, a robust model predictive control (RMPC) using semi‐homomorphic encryption is proposed to ensure the stability of polytopic uncertain systems while preserving system privacy and security. Firstly, a cloud‐based encrypted RMPC (ERMPC) framework is proposed, and semi‐homomorphic encryption is utilized to enable encrypted evaluation of control inputs. Secondly, an optimization problem is formulated to address both model uncertainties and quantization‐induced system errors, ensuring closed‐loop stability and invariance through the notion of quadratic boundedness. Thirdly, an ERMPC algorithm is presented, and conditions for selecting encryption‐related parameters are provided to guarantee the stability and bounded control performance of the encrypted cloud‐based control systems. Finally, a numerical example is conducted to demonstrate the effectiveness of the proposed approach in enhancing privacy and security in cloud‐based control systems. [ABSTRACT FROM AUTHOR]
ISSN:10498923
DOI:10.1002/rnc.70339