ResearchTrend.AI
Neural Network Quantisation for Faster Homomorphic Encryption
v1v2 (latest)

Neural Network Quantisation for Faster Homomorphic Encryption

Wouter Legiest
Jan-Pieter DÁnvers
Furkan Turan
Michiel Van Beirendonck
Ingrid Verbauwhede
    MQ
ArXiv (abs)PDFHTML

Papers citing "Neural Network Quantisation for Faster Homomorphic Encryption"

3 / 3 papers shown
Title
An End-to-End Homomorphically Encrypted Neural Network
An End-to-End Homomorphically Encrypted Neural Network
Marcos Florencio
Luiz Alencar
Bianca Lima
SyDa
139
0
0
22 Feb 2025
DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
Arjun Roy
Kaushik Roy
431
1
0
27 Aug 2024
Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data
Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data
Leonardo Neumann
Antonio Guimarães
Diego F. Aranha
Edson Borin
AAML
52
0
0
29 Mar 2024