activity
20242026
most citedStanding Firm in 5G: A Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning

2 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2026

Tick-Tock on the Open Fronthaul: Securing Synchronization in O-RAN

Yiwei Zhang, Enrico Pisanti, Imtiaz Karim +2

The Precision Time Protocol (PTP) provides the time and phase synchronization required by disaggregated Open Radio Access Networks (O-RAN). Yet, in current open fronthaul deploymen…

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.CR2026

Quantum-Resistant Networks: A Review of Primitives, Protocols and Best Practices

Elisa Bertino, Ramana Kompella, Ashish Kundu +3

Large-scale quantum computers threaten the public-key cryptographic foundations underpinning today's network security infrastructures. While significant progress has been made in s…

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

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.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…