MetaDP-HE: Dynamic Privacy-Protection With Meta-Model in End-Edge-Cloud Systems
Year: 2025
Type: article
Abstract: In distributed learning, the End-Edge-Cloud architecture is gaining widespread adoption. However, the cross-layer data interaction in EEC systems significantly increases the risk of privacy breaches. To address this challenge, this paper proposes a novel dynamic privacy-protection framework named MetaDP-HE. The framework is designed to enhance privacy protection while maintaining model performance. It integrates meta-model guided differential privacy (MetaDP) with CKKS homomorphic encryption that supports floating-point operations. A dynamic coordination mechanism is introduced to optimize the parameter configurations between MetaDP and HE. Specifically, clients use the meta-model to predict privacy budgets based on data sensitivity and adjust the noise in differential privacy accordingly. Edge servers then dynamically adjust the encryption parameters of homomorphic encryption based on the noise level, achieving adaptive regulation of encryption strength. The combination of layered encryption and dynamic parameter optimization enables the system to ensure privacy protection and efficient operations when handling data at different levels. Experimental results show that MetaDP-HE outperforms traditional single-privacy methods in both privacy protection and model performance, validating its effectiveness and applicability in practical scenarios.
Source: IEEE Internet of Things Journal
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G5438357748, G6028257120
OpenAlex ID: W4415179042