Shen Wang
PhD
Shen Wang (Member, IEEE) is currently an Assistant Professor at the School of Computer Science, University College Dublin, Ireland. He received an M.Eng. degree from Wuhan University, China, and a Ph.D. degree from Dublin City University, Ireland. Dr. Wang is a member of the IEEE and has been involved with several EU projects as a co-PI, WP, and Task leader in big trajectory data streaming for air traffic control and trustworthy AI for intelligent cybersecurity systems. Some key industry partners of his applied research are IBM Research Brazil, Boeing Research and Technology Europe, and Huawei Ireland Research Centre through various EU and state fundings such as Science Foundation Ireland (SFI). He is the recipient of the IEEE Intelligent Transportation Systems Society Young Professionals Travelling Fellowship 2022. His research interests include connected autonomous vehicles, explainable artificial intelligence, and security and privacy for mobile networks.
Publications
A survey on privacy of personal and non-personal data in B5G/6G networks
The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such asultra-high data rates, dense connectivity, and high scalability. It opens
SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks
With the rapid progression of communication and localisation of big data over billions of devices, distributed Machine Learning (ML) techniques are emerging to cater for
Rec-Def: A Recommendation-based Defence Mechanism for Privacy Preservation in Federated Learning Systems
An emergence of attention and regulations on consumer privacy can be observed over the recent years with the ubiquitous availability of IoT systems handling personal
From Opacity to Clarity: Leveraging XAI for Robust Network Traffic Classification
A wide adoption of Artificial Intelligence (AI) can be observed in recent years over networking to provide zero-touch, full autonomy of services towards the next
FL-TIA: Novel Time Inference Attacks on Federated Learning
Federated Learning (FL) is an emerging privacy-preserved distributed Machine Learning (ML) technique where multiple clients can contribute to training an ML model without sharing private
Privacy of the Metaverse: Current Issues, AI Attacks, and Possible Solutions
Metaverse is a key emerging digital transformation concept for the next generation of cyberspace. It is expected to create a self-sustaining virtual ecosystem of fully
A Survey on Privacy for B5G/6G: New Privacy Challenges, and Research Directions
Massive developments in mobile wireless telecommunication networks have been made during the last few decades. At present, mobile users are getting familiar with the latest
A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges
The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based