29 citations · 49 across the 10 of their papers we have counts for
5 papers · 1 filter
Virtual Secure Platform: A Five-Stage Pipeline Processor over TFHE
Kotaro Matsuoka, Ryotaro Banno, Naoki Matsumoto +2
We present Virtual Secure Platform (VSP), the first comprehensive platform that implements a multi-opcode general-purpose sequential processor over Fully Homomorphic Encryption (FH…
FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning
Kenta Nagura, Song Bian, Takashi Sato
Federated learning (FL) is a distributed learning protocol in which a server needs to aggregate a set of models learned some independent clients to proceed the learning process. At…
BUNET: Blind Medical Image Segmentation Based on Secure UNET
Song Bian, Xiaowei Xu, Weiwen Jiang +2
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a s…
ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition
Song Bian, Tianchen Wang, Masayuki Hiromoto +2
In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of privacy-preserving…
NASS: Optimizing Secure Inference via Neural Architecture Search
Song Bian, Weiwen Jiang, Qing Lu +2
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research inte…