2 citations · 2 across the 4 of their papers we have counts for
12 papers
A Lightweight Post-Quantum Authentication Framework for 5G Base Station Bootstrapping
Saleh Darzi, Mirza Masfiqur Rahman, Imtiaz Karim +3
The absence of authenticated bootstrapping between User Equipments (UEs) and Base Stations (BSs) in 5G leaves System Information Block (SIB) broadcasts unprotected, enabling fake B…
Future-Proofing Authentication Against Insecure Bootstrapping for 5G Networks: Feasibility, Resiliency, and Accountability
Saleh Darzi, Mirza Masfiqur Rahman, Imtiaz Karim +3
The 5G protocol lacks a robust base station (BS) authentication mechanism during the initial bootstrapping phase, leaving it susceptible to fake BSs, spoofed broadcasts, and large-…
RAG Security and Privacy: Formalizing the Threat Model and Attack Surface
Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi +1
Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce m…
Information Theoretic Adversarial Training of Large Language Models
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors under novel attack strategies.…
Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy
Rouzbeh Behnia, Jeremiah Birrell, Arman Riasi +3
Federated learning (FL) enables organizations to collaboratively train models without sharing their datasets. Despite this advantage, recent studies show that both client updates a…
Standing Firm in 5G: A Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning
Yiwei Zhang, Rouzbeh Behnia, Imtiaz Karim +2
Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that i…