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20212026
most citedHomomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

5 citations · 12 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CR2026

Quantum Key Distribution Secured Federated Learning for Channel Estimation and Radar Spectrum Sensing in 6G Networks

Ferhat Ozgur Catak, Murat Kuzlu, Jungwon Seo +1

This paper presents a federated learning framework secured by quantum key distribution (QKD) for wireless channel estimation and radar spectrum sensing in the next generation netwo…

cs.CR20241 cited

Neural Networks Meet Elliptic Curve Cryptography: A Novel Approach to Secure Communication

Mina Cecilie Wøien, Ferhat Ozgur Catak, Murat Kuzlu +1

In recent years, neural networks have been used to implement symmetric cryptographic functions for secure communications. Extending this domain, the proposed approach explores the…

cs.CR2023

Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks

Ferhat Ozgur Catak, Umit Cali, Murat Kuzlu +1

With the rise of intelligent applications, such as self-driving cars and augmented reality, the security and reliability of wireless communication systems have become increasingly…

cs.CR20225 cited

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp +2

Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the…

cs.CR20222 cited

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali +2

The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting t…