Deep learning (DL) has demonstrated superior success in various of applications, such as
image classification, speech recognition, and anomalous detection. The unprecedented …

Many different types of problems have been solved using deep learning in recent pasts.
Deep learning techniques are useful for finding solutions to different types of data type's right …

This paper aims to develop a new efficient framework named Secure Decentralized Training
Framework (SDTF) for Privacy Preserving Deep Learning models. The main feature of the …

In the recent decade, deep learning techniques have been widely adopted for founding
artificial Intelligent applications, which led to successes in many data analysis tasks, such as …

Recently, privacy-preserving machine learning (PPML) has received a lot of research
attention, due to the increasing demand for multiple data owners in training machine …

The recent advances in deep learning have improved the state of the art in artificial
intelligence, and one of the most important stimulants of this success is the large volume of …

Secure multi-party computation-based machine learning, referred to as multi-party learning
(MPL for short), has become an important technology to utilize data from multiple parties with …

The overhead of non-linear functions dominates the performance of the secure multiparty
computation (MPC) based privacy-preserving machine learning (PPML). This work …

Deep learning based on artificial neural networks is a very popular approach to modeling,
classifying, and recognizing complex data such as images, speech, and text. The …

The proliferation of deep learning (DL) has led to the emergence of privacy and security
concerns. To address these issues, secure Two-party computation (2PC) has been …