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2022 | OriginalPaper | Chapter

4. Personalized Privacy Protection of IoTs Using GAN-Enhanced Differential Privacy

Authors : Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang

Published in: Privacy Preservation in IoT: Machine Learning Approaches

Publisher: Springer Nature Singapore

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Abstract

The cyber-physical social system (CPSS), as an extension of Internet of Things (IoT), maps human social interaction from cyberspace to the physical world by data sharing and posting, such as publishing spatial-temporal data, images, videos, etc. The published data contains individual’s sensitive information, and thereby leads to continues attacks on it. Nowadays, most existing research assumes that the privacy protection should be uniform that all parties share a same privacy protection level. This impractical assumption results in data degradation due to over-protection or privacy leakage due to under-protection.

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previous chapter Decentralized Privacy Protection of IoTs Using Blockchain-Enabled Federated Learning
next chapter Hybrid Privacy Protection of IoT Using Reinforcement Learning
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Metadata
Title
Personalized Privacy Protection of IoTs Using GAN-Enhanced Differential Privacy
Authors
Youyang Qu
Longxiang Gao
Shui Yu
Yong Xiang
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Privacy Preservation in IoT: Machine Learning Approaches

Print ISBN: 978-981-19-1796-7

Electronic ISBN: 978-981-19-1797-4

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-19-1797-4_4

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