Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data

Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data

Homomorphic encryption (HE) is one of the representative solutions to privacy-preserving machine learning (PPML) classification enabling the server to classify private data of clients while guaranteeing privacy. This work focuses on PPML using word-wise fully homomorphic encryption (FHE). In order t...

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Bibliographic Details
Main Authors: Junghyun Lee, Eunsang Lee, Joon-Woo Lee, Yongjune Kim, Young-Sik Kim, Jong-Seon No
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Fully homomorphic encryption
RNS-CKKS
privacy-preserving machine learning
deep learning
cloud computing
Online Access:https://ieeexplore.ieee.org/document/10155408/

Internet

https://ieeexplore.ieee.org/document/10155408/

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