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2019 | OriginalPaper | Buchkapitel

Privacy-Preserving All Convolutional Net Based on Homomorphic Encryption

verfasst von : Wenchao Liu, Feng Pan, Xu An Wang, Yunfei Cao, Dianhua Tang

Erschienen in: Advances in Network-Based Information Systems

Verlag: Springer International Publishing

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Abstract

Machine learning servers with mass storage and computing power is an ideal platform to store, manage, and analyze data and support decision-making. However, the main issue is providing security and privacy to the data, as the data is stored in a public way. Recently, homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. In this paper, we proposed an encrypted all convolutional net that transformed traditional all convolutional net into a net based on homomorphic encryption. This scheme allows different data holders to send their encrypted data to cloud service, complete predictions, and return them in encrypted form as the cloud service provider does not have a secret key. Therefore, the cloud service provider and others cannot get unencrypted raw data. When applied to the MNIST database, privacy-preserving all convolutional based on homomorphic encryption predict efficiently, accurately and with privacy protection.

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Titel: Privacy-Preserving All Convolutional Net Based on Homomorphic Encryption
verfasst von: Wenchao Liu
Feng Pan
Xu An Wang
Yunfei Cao
Dianhua Tang
Verlag: Springer International Publishing
Buch: Advances in Network-Based Information Systems
Print ISBN: 978-3-319-98529-9

Electronic ISBN: 978-3-319-98530-5

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-98530-5_66

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