With the ever-increasing requirement of storage and computation resources, it is unrealistic
for local devices (with limited sources) to implement large-scale data processing. Therefore …

In this paper, we propose a privacy-preserving image classification method that is based on
the combined use of encrypted images and the vision transformer (ViT). The proposed …

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 …

To implement image storage and computation in cloud servers without violating users'
privacy, privacy-preserving feature extraction has been a new research interest. The existing …

With the increasing severity of user privacy leaks in online social networks (OSNs), existing
privacy protection technologies have difficulty meeting the diverse privacy protection needs …

Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly
important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is …

With outsourcing huge amount of images to cloud, it can reduce the local storage and
computational overhead of resource-limited users, but the security concerns still impede the …

In this paper, we propose an accurate pedestrian detection scheme with privacy
preservation (PPPD) based on pedestrian images. By utilizing the vector homomorphic …

Feature selection is a crucial process in data science that involves selecting the most
effective subset of features. Evolutionary computation (EC) is one of the most commonly …

In this paper, we propose an extraction method of HOG (histograms-of-oriented-gradients)
features from encryption-then-compression (EtC) images for privacy-preserving machine …