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20192026
most citedPrivately Fine-Tuning Large Language Models with Differential Privacy

55 citations · 80 across the 18 of their papers we have counts for

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16 papers · 1 filter

cs.CR2026

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…

cs.CR2025

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…

cs.CR2025

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-…

cs.CR2025★ 2 cited

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…

cs.CR2025★ 2 cited

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…

cs.CR2025

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…

cs.CR2025★ 1 cited

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…

cs.CR2024

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…

cs.CR2024

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…

cs.CR2023★ 1 cited

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…

cs.CR2022★ 55 cited

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…

cs.CR2022★ 1 cited

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…

cs.CR2021

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…

cs.CR2019★ 16 cited

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…

cs.CR2019

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…

cs.CR2019

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…