Enhancing privacy in Generative AI-enabled consumer electronics using homomorphic encryption and federated learning

Gao, Zheng, Li, Shancang and Iqbal, Muddear 2025. Enhancing privacy in Generative AI-enabled consumer electronics using homomorphic encryption and federated learning. IEEE Transactions on Consumer Electronics 10.1109/tce.2025.3597357

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Abstract

The integration of Generative AI (GAI) into consumer electronics (e.g., smart homes, wearables) introduces critical privacy risks as sensitive user data fuels personalized services. This paper proposes a homomorphic encryption-federated learning (HE-FL) framework that ensures end-to-end data confidentiality and decentralized model training. By combining HE’s encrypted computation with FL’s distributed architecture, the framework mitigates vulnerabilities in centralized systems while resisting probabilistic polynomial-time adversaries under the Learning With Errors (LWE) assumption. Evaluations on MNIST demonstrate a 3% accuracy trade-off (95.5% vs. 98.5% baseline) for robust privacy, reducing gradient inversion success to ≤5%. Case studies in healthcare wearables and smart grids validate QoS-aware risk mitigation. Challenges in scalability and quantum-era security are addressed through edge-assisted optimizations and hybrid architectures, aligning with GDPR/CCPA compliance to foster trust in GAI-driven ecosystems.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0098-3063
Last Modified:	27 Aug 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/180684

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