High-Order Vehicular Pattern Learning and Privacy-Preserving and Unsupervised GAN for Privacy Protection Toward Vehicular Parts Detection

High-Order Vehicular Pattern Learning and Privacy-Preserving and Unsupervised GAN for Privacy Protection Toward Vehicular Parts Detection

This paper introduces High-order Vehicular Pattern Learning (HVPL), a novel framework designed to enhance vehicular pattern detection while ensuring privacy protection, associated with authentication through the integration of Privacy-Preserving and Unsupervised GAN (PPUP-GAN). To preserve data priv...

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Bibliographic Details
Main Authors: Yanqin Zhang, Zhanling Zhang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Vehicular pattern detection
privacy protection
high-order vehicular pattern learning
privacy-preserving and unsupervised GAN
hypergraph modeling
Online Access:https://ieeexplore.ieee.org/document/11029001/
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https://ieeexplore.ieee.org/document/11029001/

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