6 papers
Securing LLMs in the Wild: Privacy and Security Challenges at the Edge
Ren-Yi Huang, Mingchen Li, Dumindu Samaraweera +1
Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms. While cloud deplo…
Building Trust in the Skies: A Knowledge-Grounded LLM-based Framework for Aviation Safety
Anirudh Iyengar, Alisa Tiselska, Dumindu Samaraweera +1
The integration of Large Language Models (LLMs) into aviation safety decision-making represents a significant technological advancement, yet their standalone application poses crit…
Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Ren-Yi Huang, Dumindu Samaraweera, Prashant Shekhar +1
Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping raw data locally stored on client devices, preventing access from other client…
Secure Distributed Learning for CAVs: Defending Against Gradient Leakage with Leveled Homomorphic Encryption
Muhammad Ali Najjar, Ren-Yi Huang, Dumindu Samaraweera +1
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine lear…
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
Mingchen Li, Di Zhuang, Keyu Chen +2
Link prediction in graph data uses various algorithms and Graph Nerual Network (GNN) models to predict potential relationships between graph nodes. These techniques have found wide…
Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous Driving
Prashant Shekhar, Bidur Devkota, Dumindu Samaraweera +2
Adversarial attacks pose a significant threat to deep learning models, particularly in safety-critical applications like healthcare and autonomous driving. Recently, patch based at…