Chamara Sandeepa
BSc Eng.(Hons)
Chamara Sandeepa is currently a PhD student in the School of Computer Science, University College Dublin, Ireland, and a Doctoral Student/Research Engineer of the EU H2020 SPATIAL project. He is currently working in the field of privacy aspects of AI. He received his Bachelor’s degree in Electrical and Information Engineering from the University of Ruhuna, Sri Lanka, in 2020. During his undergraduate period and later work, he actively contributed to research and published in multiple conferences and journals. He has professional experience in Software Engineering and worked in the fields of IoT and AI.
Publications
Netslab-5GORAN-IDD
The Netslab-5G Open RAN Intrusion Detection Dataset (Netslab-5GORAN-IDD) is a real-world dataset collected using a physical 5G Open RAN testbed at University College Dublin. It
Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges
The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given
DisLLM: Distributed LLMs for Privacy Assurance in Resource-Constrained Environments
Large Language Models (LLMs) have revolutionized natural language processing, but deploying them in resource-constrained environments and privacy-sensitive domains remains challenging. This paper introduces the Distributed
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
Privacy-preserved Collaborative Federated Learning Platform for Industrial Internet of Things
Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability
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
Energy Efficient Contact Tracing and Social Interaction based Patient Prediction System for COVID-19 Pandemic
Due to the spread of Coronavirus disease 2019 (COVID-19), the world has encountered an ongoing pandemic to date. It is a highly contagious disease. In
Security enhanced Emergency Situation Detection System for Ambient Assisted LivingSystem for Ambient Assisted Living
Typical wearable devices use a dedicated mobile phone as relay node to transfer the collected sensor data toa server. However, such relay nodes can be
Social Interaction Tracking and Patient Prediction System for Potential COVID-19 Patients
Coronavirus disease 2019 (COVID-19) virus is an infectious disease which has spread globally since 2019, resulting in an ongoing pandemic. Since it is a new
An Emergency Situation Detection System for Ambient Assisted Living
This paper proposes “An Emergency Situation Detection System for Ambient Assisted Living (AAL)”, to support elderly people and patients with chronic conditions and potential health-related