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pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. -
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and... -
MNIST, USPS, and CIFAR10
The dataset used in this paper is MNIST, USPS, and CIFAR10. The dataset is used for privacy-preserving CNN training. -
Human Action Recognition By Ultrasound Active Sensing
Action recognition is a key technology for many industrial applications. Methods using visual information such as images are very popular. However, privacy issues prevent... -
Privacy-Preserving Image Classification Using Vision Transformer
Privacy-preserving image classification method that uses ViT and a block-wise encryption method
