6 papers
Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings
Nora Basha, Bechir Hamdaoui, Attila A. Yavuz +2
Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on th…
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…
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…