From the 1 of 6 linked papers with an AI index.
6 papers
Securing LLMs in the Wild: Privacy and Security Challenges at the Edge
Ren-Yi Huang, Mingchen Li, Dumindu Samaraweera +1
The paper examines how efficiency‑focused optimizations for deploying large language models on edge devices create new security and privacy vulnerabilities, and it introduces a tax…
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…
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…
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…
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…