6 papers
SoK: Practical Aspects of Releasing Differentially Private Graphs
Nicholas D'Silva, Surya Nepal, Salil S. Kanhere
Graph data is increasingly prevalent across domains, offering analytical value but raising significant privacy concerns. Edges may encode sensitive relationships, while node attrib…
Does Teaming-Up LLMs Improve Secure Code Generation? A Comprehensive Evaluation with Multi-LLMSecCodeEval
Bushra Sabir, Shigang Liu, Seung Ick Jang +6
Automatically generating source code from natural language using large language models (LLMs) is becoming common, yet security vulnerabilities persist despite advances in fine tuni…
Security in the Era of Perceptive Networks: A Comprehensive Taxonomic Framework for Integrated Sensing and Communication Security
Chandra Thapa, Surya Nepal
Integrated Sensing and Communication (ISAC) represents a significant shift in the 6G landscape, where wireless networks both sense the environment and communicate. While prior comp…
Future G Network's New Reality: Opportunities and Security Challenges
Chandra Thapa, Surya Nepal
Future G network's new reality is a widespread cyber-physical environment created by Integrated Sensing and Communication (ISAC). It is a crucial technology that transforms wireles…
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen +3
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable…
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning
Hassan Ali, Surya Nepal, Salil S. Kanhere +1
Recent works have shown that Federated Learning (FL) is vulnerable to backdoor attacks. Existing defenses cluster submitted updates from clients and select the best cluster for agg…