Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches. Although FHE schemes are suitable as tools for implementing PPML models, previous PPML models based on FHE, suc...

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Bibliographic Details
Main Authors: Joon-Woo Lee, Hyungchul Kang, Yongwoo Lee, Woosuk Choi, Jieun Eom, Maxim Deryabin, Eunsang Lee, Junghyun Lee, Donghoon Yoo, Young-Sik Kim, Jong-Seon No
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Privacy-preserving machine learning
ResNet-20
RNS-CKKS FHE scheme
SEAL library
software implementation
Online Access:https://ieeexplore.ieee.org/document/9734024/

Internet

https://ieeexplore.ieee.org/document/9734024/

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