Due to the risk of data leakage while training deep learning models in a shared
environment, we propose a new privacy-preserving deep learning (PPDL) method using a …

With the emergence of cloud computing, large amounts of private data are stored and
processed in the cloud. On the other hand, data owners (users) may not want to reveal data …

Deep learning (DL) has shown outstanding performances in many fields, including remote
sensing (RS). DL is turning into an essential tool for the RS research community. Recently …

With the increasing prevalence of cloud computing platforms, ensuring data privacy during
the cloud-based image-related services such as classification has become crucial. In this …

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 …

In this article, we propose a privacy-preserving image classification method that uses
encrypted images and an isotropic network, such as the vision transformer. The proposed …

In recent years, privacy-preserving methods for deep learning have become an urgent
problem. Accordingly, we propose the combined use of federated learning (FL) and …

The widespread adoption of deep learning (DL) solutions in the healthcare organizations is
obstructed by their compute intensive nature and dependability on massive datasets. In this …

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that
enables us not to only apply images without visual information to DNNs for both training and …

In this paper, we propose a privacy-preserving image classification method using block-wise
scrambled images and a modified ConvMixer. Conventional block-wise scrambled …