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ENClose: Encrypted Nonlinear Closed-Loop Control Over Fully Homomorphic Encryption

  • Song Bian
  • , Yuexiang Jin
  • , Dong Zhao
  • , Yunhao Fu
  • , Haowen Pan
  • , Yi Chen
  • , Bo Zhang
  • , Changrui Ren
  • , Peng Yin
  • , Jin Dong*
  • , Zhenyu Guan
  • *此作品的通讯作者
  • Beihang University
  • Beijing Academy of Blockchain and Edge Computing
  • Defence Industry Secrecy Examination and Certification Center

科研成果: 期刊稿件文章同行评审

摘要

This work proposes an encrypted controller framework for closed-loop control systems with nonlinear dynamics over fully homomorphic encryption (FHE). Unlike differential privacy and output masking, FHE is a cryptographic primitive that provides assumption-based confidentiality guarantees under standard hardness assumptions. We observe that existing encrypted control frameworks remain largely limited to linear open-loop systems, primarily due to two key challenges: rapid ciphertext noise accumulation in feedback loops and the substantial computational overhead of nonlinear operations. In control systems, feedback is essential for real-time error correction, while nonlinear characteristics are critical for accurately modelling complex system behaviours. To address these challenges, we propose ENClose, a novel encrypted control framework that enables low-latency execution of both feedback control and nonlinear function evaluation. Specifically, ENClose introduces a low-latency homomorphic nonlinear computation framework that accelerates functional bootstrapping (FBS) by combining function segmentation with tree-based encrypted selection. This framework not only mitigates noise accumulation in encrypted feedback loops but also significantly improves the efficiency of FBS under high-precision settings, meeting the computational demands of dynamic control systems. Experimental results show that ENClose achieves a 3× to 20× speedup over state-of-the-art encrypted controllers. We validate ENClose through real-world applications, including multi-vehicle formation, spring–mass–damper control, and anomaly recovery, where the results demonstrate high-precision tracking and successful reconvergence after anomalies.

源语言英语
页(从-至)3928-3943
页数16
期刊IEEE Transactions on Information Forensics and Security
21
DOI
出版状态已出版 - 2026

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