55 citations · 80 across the 18 of their papers we have counts for
20 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…
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…
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…
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…
Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
Yiwei Zhang, Rouzbeh Behnia, Attila A. Yavuz +2
Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy at…
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models
Kasra Ahmadi, Rouzbeh Behnia, Reza Ebrahimi +4
Federated learning (FL) enhances privacy by keeping user data on local devices. However, emerging attacks have demonstrated that the updates shared by users during training can rev…
Securing 5G Bootstrapping: A Two-Layer IBS Authentication Protocol
Yilu Dong, Rouzbeh Behnia, Attila A. Yavuz +1
The lack of authentication during the initial bootstrapping phase between cellular devices and base stations allows attackers to deploy fake base stations and send malicious messag…
From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
Yuncong Yang, Xiao Han, Yidong Chai +3
Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, c…
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
Yiwei Zhang, Rouzbeh Behnia, Attila A. Yavuz +2
Federated learning enables the collaborative learning of a global model on diverse data, preserving data locality and eliminating the need to transfer user data to a central server…
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
Jeremiah Birrell, Reza Ebrahimi, Rouzbeh Behnia +1
Differentially private stochastic gradient descent (DP-SGD) has been instrumental in privately training deep learning models by providing a framework to control and track the priva…
Unsupervised Threat Hunting using Continuous Bag-of-Terms-and-Time (CBoTT)
Varol Kayhan, Shivendu Shivendu, Rouzbeh Behnia +2
Threat hunting is sifting through system logs to detect malicious activities that might have bypassed existing security measures. It can be performed in several ways, one of which…
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
Rouzbeh Behnia, Arman Riasi, Reza Ebrahimi +3
Secure aggregation protocols ensure the privacy of users' data in federated learning by preventing the disclosure of local gradients. Many existing protocols impose significant com…
Privately Fine-Tuning Large Language Models with Differential Privacy
Rouzbeh Behnia, Mohamamdreza Ebrahimi, Jason Pacheco +1
Pre-trained Large Language Models (LLMs) are an integral part of modern AI that have led to breakthrough performances in complex AI tasks. Major AI companies with expensive infrast…
FROG: Forward-Secure Post-Quantum Signature
Attila A. Yavuz, Rouzbeh Behnia
Forward-secure signatures guarantee that the signatures generated before the compromise of private key remain secure, and therefore offer an enhanced compromise-resiliency for real…
Compatible Certificateless and Identity-Based Cryptosystems for Heterogeneous IoT
Rouzbeh Behnia, Attila A. Yavuz, Muslum Ozgur Ozmen +1
Certificates ensure the authenticity of users' public keys, however their overhead (e.g., certificate chains) might be too costly for some IoT systems like aerial drones. Certifica…
IoD-Crypt: A Lightweight Cryptographic Framework for Internet of Drones
Muslum Ozgur Ozmen, Rouzbeh Behnia, Attila A. Yavuz
Internet of Drones (IoD) is expected to play a central role in many civilian and military applications, that require sensitive and mission-critical information to be processed. It…
Energy-Aware Digital Signatures for Embedded Medical Devices
Muslum Ozgur Ozmen, Attila A. Yavuz, Rouzbeh Behnia
Authentication is vital for the Internet of Things (IoT) applications involving sensitive data (e.g., medical and financial systems). Digital signatures offer scalable authenticati…
ARIS: Authentication for Real-Time IoT Systems
Rouzbeh Behnia, Muslum Ozgur Ozmen, Attila A. Yavuz
Efficient authentication is vital for IoT applications with stringent minimum-delay requirements (e.g., energy delivery systems). This requirement becomes even more crucial when th…