Fhepdm: Fully homomorphic encryption secured collaborative anomaly detection in wind turbines predictive maintenance

Dai, Jinxiang and Li, Shancang 2026. Fhepdm: Fully homomorphic encryption secured collaborative anomaly detection in wind turbines predictive maintenance. Journal of Management Analytics 10.1080/23270012.2026.2640589

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Abstract

In wind power generation, equipment anomalies and cyber threats impose substantial operational and economic risks. While multi-party data sharing among turbine manufacturers, operators, and analytics providers could improve anomaly detection, privacy issues restrict collaboration. This paper proposes FHEPdM (Fully Homomorphic Encryption-enabled Secure Collaborative Anomaly Detection Architecture), a novel framework enabling privacy-preserving multi-party predictive maintenance without revealing sensitive data. Utilizing fully homomorphic encryption (FHE), it trains and infers a lightweight logistic regression model on encrypted Supervisory Control and Data Acquisition (SCADA) data for end-to-end confidentiality. Integrated with a hybrid Elliptic Curve Diffie-Hellman-fully homomorphic encryption (ECDH-FHE) key management system, it reduces latency while upholding 128-bit security. Evaluation on the Wind Turbine SCADA Dataset yields 0.979 accuracy, 0.52 membership inference attack success, and 17-minute training, delivering a practical, scalable solution for secure industrial cooperation in wind systems.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computational & Mathematical Sciences
Publisher:	Taylor and Francis Group
ISSN:	2327-0012
Date of First Compliant Deposit:	6 August 2026
Date of Acceptance:	28 February 2026
Last Modified:	06 Aug 2026 09:15
URI:	https://orca.cardiff.ac.uk/id/eprint/188755

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