4 papers
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Xavier Martínez-Luaña, Alba Gude-Santos, Manuel Fernández-Veiga +1
Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation. E…
Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning
Xavier Martínez-Luaña, Manuel Fernández-Veiga, Rebeca P. Díaz-Redondo +1
Coded computing is one of the techniques that can be used for privacy protection in Federated Learning. However, most of the constructions used for coded computing work only under…
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo +5
The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious…
Analysis of Efficiency of the Messaging Layer Security protocol in Experimental Settings
David Soler, Carlos Dafonte, Manuel Fernández-Veiga +2
Messaging Layer Security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such t…