支持隐私保护的智能物联网数据风格转换

doi:10.16180/j.cnki.issn1007-7820.2024.10.001

Abstract

Abstract:

In the artificial intelligence of things, traditional privacy protection technologies mainly focus on the transmission, storage, and analysis stages of the data lifecycle, while ignoring the importance of protecting data privacy at the source. This study proposes a privacy-protecting data style transfer method for artificial intelligence of things. Based on cycle-consistent adversarial networks, a new loss function is added to obfuscate identity information, allowing real-style images and animation-style images to visually transform into each other. Animation-style data can be used to construct various virtual entities in the digital world (such as metaverses), and malicious users cannot reverse the original data based on the virtual entities or correctly identify the original data using the original deep learning model, thereby enhancing privacy protection for real entities in the physical world. Experimental results on a face dataset show that the transformed data reduces the accuracy of the ArcFace face recognition model by 30% without significantly reducing visual distortion.

Key words: style transfer, adversarial examples, face recognition, CycleGAN, digital twin, artificial intelligence of things, privacy protection, metaverse

CLC Number:

TP391

CHENG Jinke, LI Gaolei. Privacy-Preserving Data Style Transfer Method for Artificial Intelligence of Things[J].Electronic Science and Technology, 2024, 37(10): 1-5.

Figures/Tables 6

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Table 1.

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References 25

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人脸识别模型	FaceNet	ArcFace
原始CycleGAN	96.1	98.8
身份混淆CycleGAN	19.3	60.9

Privacy-Preserving Data Style Transfer Method for Artificial Intelligence of Things

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