CHE: Channel-Wise Homomorphic Encryption for Ciphertext Inference in Convolutional Neural Network
Privacy-preserving deep learning (PPDL), which leverages Homomorphic Encryption (HE), has attracted attention as a promising approach to ensure the privacy of deep learning applications’ data. While recent studies have developed and evaluated the HE-based PPDL algorithms, the achieved per...
Main Authors: | Tianying Xie, Hayato Yamana, Tatsuya Mori |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9903645/ |
Similar Items
-
Research and design of CRT-based homomorphic ciphertext database system
by: De Zhao, et al.
Published: (2022-12-01) -
HeFUN: Homomorphic Encryption for Unconstrained Secure Neural Network Inference
by: Duy Tung Khanh Nguyen, et al.
Published: (2023-12-01) -
An Efficient Ciphertext Retrieval Scheme Based on Homomorphic Encryption for Multiple Data Owners in Hybrid Cloud
by: Heng He, et al.
Published: (2021-01-01) -
Privacy-Preserving Federated Learning Using Homomorphic Encryption
by: Jaehyoung Park, et al.
Published: (2022-01-01) -
Analysis of Gong et al.'s CCA2-secure homomorphic encryption
by: Lee, Hyung Tae, et al.
Published: (2017)