Deep learning is one of the advanced approaches of machine learning, and has attracted a
growing attention in the recent years. It is used nowadays in different domains and …

Data mining information about people is becoming increasingly important in the data-driven
society of the 21st century. Unfortunately, sometimes there are real-world considerations that …

This paper studies how a system operator and a set of agents securely execute a distributed
projected gradient-based algorithm. In particular, each participant holds a set of problem …

This article studies the problem of distributed optimization in multiagent systems where each
agent seeks to minimize the sum of all agents' objective functions using only local …

Because of the explosive growth of face photos as well as their widespread dissemination
and easy accessibility in social media, the security and privacy of personal identity …

Data sharing is a central aspect of judicial systems. The openly accessible documents can
make the judiciary system more transparent. On the other hand, the published legal …

Benefitting from the outstanding capabilities of intelligent controlling and prediction,
federated learning (FL) has been widely applied in Internet of Vehicle (IoV). However …

Training supervised machine learning models like deep learning requires high-quality
labelled datasets that contain enough samples from various categories and specific cases …

Payment channel network (PCN) is a promising off-chain technology for blockchain
scalability, but it suffers from poor sustainability in practice. In other words, due to the …

In this work, we study the applications of differential privacy (DP) in the context of graph-
structured data. We discuss the formulations of DP applicable to the publication of graphs …