88 citations · 102 across the 3 of their papers we have counts for
6 papers
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Chuan Ma, Jun Li, Ming Ding +4
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. Without…
Blockchain as a Service for Multi-Access Edge Computing: A Deep Reinforcement Learning Approach
Dinh C Nguyen, Pubudu N Pathirana, Ming Ding +1
Recently, blockchain has gained momentum in the academic community thanks to its decentralization, immutability, transparency and security. As an emerging paradigm, Multi-access Ed…
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Kang Wei, Jun Li, Ming Ding +6
In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to t…
On Safeguarding Privacy and Security in the Framework of Federated Learning
Chuan Ma, Jun Li, Ming Ding +4
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) pa…
Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges
Dinh C Nguyen, Pubudu N Pathirana, Ming Ding +1
The blockchain technology is taking the world by storm. Blockchain with its decentralized, transparent and secure nature has emerged as a disruptive technology for the next generat…
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding +1
Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the mining process in mobile blockchain…