17 citations · 57 across the 24 of their papers we have counts for
5 papers · 1 filter
A Random Ensemble of Encrypted models for Enhancing Robustness against Adversarial Examples
Ryota Iijima, Sayaka Shiota, Hitoshi Kiya
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source…
Enhanced Security with Encrypted Vision Transformer in Federated Learning
Rei Aso, Sayaka Shiota, Hitoshi Kiya
Federated learning is a learning method for training models over multiple participants without directly sharing their raw data, and it has been expected to be a privacy protection…
Enhanced Security against Adversarial Examples Using a Random Ensemble of Encrypted Vision Transformer Models
Ryota Iijima, Miki Tanaka, Sayaka Shiota +1
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source…
A Privacy-Preserving Machine Learning Scheme Using EtC Images
Ayana Kawamura, Yuma Kinoshita, Takayuki Nakachi +2
We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption metho…
Privacy-Preserving SVM Computing by Using Random Unitary Transformation
Takahiro Maekawa, Takayuki Nakachi, Sayaka Shiota +1
A privacy-preserving Support Vector Machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has so…